CN116401618A - Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling - Google Patents
Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling Download PDFInfo
- Publication number
- CN116401618A CN116401618A CN202310196450.1A CN202310196450A CN116401618A CN 116401618 A CN116401618 A CN 116401618A CN 202310196450 A CN202310196450 A CN 202310196450A CN 116401618 A CN116401618 A CN 116401618A
- Authority
- CN
- China
- Prior art keywords
- precision
- unmanned carrier
- low
- unmanned
- distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005070 sampling Methods 0.000 title claims abstract description 46
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 82
- 238000000034 method Methods 0.000 claims abstract description 19
- 239000000969 carrier Substances 0.000 claims description 42
- 238000004364 calculation method Methods 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 24
- 238000012546 transfer Methods 0.000 claims description 17
- 238000001914 filtration Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 4
- 230000004927 fusion Effects 0.000 abstract 1
- 238000005457 optimization Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 239000002245 particle Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 101710103613 Flightin Proteins 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/29—Graphical models, e.g. Bayesian networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Radar, Positioning & Navigation (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Mathematical Physics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Remote Sensing (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Computational Mathematics (AREA)
- Computing Systems (AREA)
- Automation & Control Theory (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, which is characterized in that geometrical observation model components such as a sphere, a ray, a vector, an azimuth plane, a look-up plane and the like are established according to different types of relative measurement information, and combined posterior distribution sampling of positioning solutions is carried out based on the model, so that the collaborative positioning problem of the cross-domain unmanned cluster is converted into an inference problem on a message transmission model of a mixed geometric distribution sampling component, and the fusion of self-position information and relative measurement information of a cross-domain unmanned cluster carrier is realized by a geometric probability distribution sampling or probability distribution product method, thereby completing the collaborative positioning enhancement of a low-precision carrier. The method and the device can remarkably improve the overall positioning performance of the cluster under the condition of complex relative measurement information, and are suitable for practical application.
Description
Technical Field
The invention relates to the technical field of positioning and navigation, in particular to a cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling.
Background
In recent years, cross-domain unmanned cluster technology is rapidly developed and is widely applied to various fields. The problem of self-positioning of the cross-domain unmanned cluster and the problem of positioning of targets are research hotspots at home and abroad all the time, are widely applied to the fields of daily life, aerospace, military and the like, and are vital to the completion of complex tasks.
However, in complex terrain or electromagnetic interference environments, positioning signals of the reference unmanned carrier and the navigation satellite are often shielded or interfered, which seriously reduces positioning accuracy of part of members in the cross-domain unmanned cluster. Therefore, in environments where terrain shielding or complex electromagnetic interference exists, the positioning performance of the cross-domain unmanned cluster on the cross-domain unmanned cluster and the target can be greatly reduced.
In the existing co-location algorithm based on message transmission, the traditional non-parameter (non-parameteric belief propagation) algorithm utilizes the particle approximation to contain the relative measurement co-information in the message transmission process, so that a better location effect is obtained, but the overall performance of the system cannot be maintained when the number of particles is small; the traditional sigma-point method uses sigma-points to approximate nonlinear functions, has smaller calculated amount, is suitable for two-dimensional positioning with lower space dimension, and has less application in collaborative positioning in complex three-dimensional environment.
Disclosure of Invention
The invention aims to solve the technical problem of providing a cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling aiming at the defects related to the background technology.
The invention adopts the following technical scheme for solving the technical problems:
a cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling comprises the following steps:
step 1), acquiring longitude, latitude and altitude of a cross-domain unmanned cluster required by co-location and relative measurement data among the cross-domain unmanned clusters, wherein the relative measurement data comprises a distance, an azimuth angle and an altitude angle;
step 2), converting navigation parameters of longitude, latitude and altitude of the cross-domain unmanned cluster into rectangular coordinates (x, y, z) in an earth coordinate system;
step 3), estimating the positioning error of the cross-domain unmanned cluster according to the positioning type of the cross-domain unmanned cluster, and estimating the position covariance;
step 4), setting an accuracy dividing threshold tau according to the positioning error estimation of the unmanned carrier, setting the unmanned carrier with the position covariance estimation larger than tau as a low-accuracy unmanned carrier, and setting the unmanned carrier with the position covariance estimation smaller than or equal to tau as a high-accuracy unmanned carrier;
step 5), obtaining probability distribution of unmanned carrier positions, and establishing position variable nodes in a message transfer model according to the mean value and covariance of the positions;
step 6), traversing the relative measurement information of the high-precision unmanned carriers and the low-precision unmanned carriers according to the relative measurement information between the high-precision unmanned carriers and the low-precision unmanned carriers and the position estimation of each unmanned carrier, and establishing corresponding cooperative measurement information according to the type of the relative measurement information:
step 6.1), if the relative measurement information is distance information, establishing a distance measurement cooperative measurement message eta in a message transfer model based on a spherical distribution geometric model ρ ；
Step 6.2), if the relative measurement information is the line of sight information, establishing a geometrical observation model component message eta in the message transfer model based on the ray distribution ξ ；
Step 6.3) if the relative measurement information is ranging/line-of-sight angle information, establishing geometrical observations in the message transfer model based on vector distributionModel component message eta γ ；
Step 6.4), if the relative measurement information is azimuth information, establishing a geometrical observation model component message eta in the message transfer model based on azimuth plane distribution θ ；
Step 6.5), if the relative measurement information is the altitude angle information, establishing a geometrical observation model assembly message eta in the message transfer model based on the upward-looking surface distribution model e ；
And 7) fusing the positions of the high-precision unmanned carrier and the low-precision unmanned carrier with the relative measurement information through a geometric probability distribution sampling and probability distribution product algorithm, and obtaining the precise positions of the low-precision unmanned carrier.
As a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the specific steps of the step 3) are as follows:
step 3.1), if the positioning type of the unmanned carrier is geometric positioning, the error equation set of navigation positioning is expressed as:
R-P＝G u ·X u
wherein R= (R) 1 r 2 … r n ) The geometric distance from the unmanned carrier to be positioned to the geometric positioning reference station m is more than or equal to 1 and less than or equal to n; p= (ρ) 1 ρ 2 … ρ n ) A measurement pseudo range from the unmanned carrier to be positioned to the geometric positioning reference station m;
wherein (h) m1 h m2 h m3 ) Cosine, X of three directions from the unmanned carrier to be positioned to the m vector of the geometric positioning reference station u ＝[δx δy δz δu t ]，[δx δy δz]To locate position fix number δu t Is a range error caused by clock error;
order the
The geometric error coefficients of the position of the unmanned carrier to be positioned are:
the variance of the geometric positioning range error is
The covariance Σ of its three-dimensional position p The estimated values of (2) are as follows:
step 3.2), if the positioning type of the unmanned carrier is Kalman filtering integrated navigation, according to a Kalman filtering position covariance matrix P k Estimating three-dimensional position error thereof, covariance Σ of three-dimensional position thereof p The estimated values of (2) are as follows:
wherein lambda is,
h is longitude, latitude, altitude, respectively, obtained by Kalman filtering>
Respectively Kalman filtering position covariance matrix P k The variance of longitude in (1), the covariance of latitude and longitude, the variance of latitude and the variance of altitude, R is the curvature radius of the earth;
step 3.3), if the positioning type of the unmanned carrier is the calculation formula positioning, as the calculation formula positioning error diverges along with time, the error delta lambda of the inertial navigation system in the longitudinal direction is estimated according to the drift parameter epsilon of the constant value of the inertial navigation gyro of the unmanned carrier and the inertial navigation working time t, and the calculation formula is as follows:
according to the radius R of the sphere of the earth e Setting a covariance sigma of the three-dimensional position of the unmanned carrier to be positioned p Matrix:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 5) are as follows:
step 5.1), the coordinates of the ith unmanned carrier under the earth coordinate system are (x) i ,y i ,z i ) The position covariance estimate is
Approximating the position distribution of the unmanned carrier by using the multivariate normal distribution, the probability distribution of the positions of the ith unmanned carrier is:
wherein X is i Probability distribution, μ for i-th unmanned carrier position Xi ＝(x i ,y i ,z i ) The position mean value of the ith unmanned carrier is obtained, and N is normal distribution in probability statistics;
step 5.2), in the graph model, using b (X i ) Belief status indicating the position of the ith unmanned carrier itself:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 6.1) are as follows:
let the distance measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be ρ k→j Belief of j for low precision flightIn the state of
The belief state of the high-precision unmanned carrier k is
If the distance information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the distance information rho is used for measuring the distance information k→j Defining the distance message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a spherical distribution with the sphere center at +.>
Average radius ρ k→j The sphere thickness is->
For the variance of the relative ranging between high and low precision unmanned carriers, I is an identity matrix, and the formula of the spherical probability distribution is as follows:
in the middle of
Representing the calculation vector +.>
The probability distribution is:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the specific process of the step 6.2) is as follows:
let the angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be xi k→j ＝[θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
If the angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the angle information xi is measured according to k→j ＝[θ,e]Defining the angle message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
θ, e are the azimuth, altitude angle, and +.>
The position distribution of the low-precision unmanned carrier j is regarded as a probability distribution in the direction of the rays whose end points are located +.>
Average radius of +.>
Distribution variance of->
The variance of the relative sight angle measurement between high and low precision unmanned carriers is represented by I, wherein I is an identity matrix, and the formula of the probability distribution of the rays is as follows:
in the middle of
Representing the calculation vector +.>
Is a binary norm of (c).
As a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 6.3) are as follows:
let the distance between the low-precision unmanned carrier j and the high-precision unmanned carrier k be gamma k→j ＝[ρ,θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is +.>
If the distance and the sight angle information are measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the distance and the sight angle information gamma are used for being based on k→j ＝[ρ,θ,e]Defining the distance and angle measurement information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution at the end of the vector whose origin is at +.>
Average radius ρ, distribution variance +.>
The variance of the relative sight angle between high and low precision unmanned carriers is represented by the formula of the vector probability distribution, wherein I is an identity matrix:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 6.4) are as follows:
let the azimuth angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be theta k→j The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
If the azimuth angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the ranging angle information theta is used for measuring k→j Defining azimuth direction information transmitted between a high-precision unmanned carrier k and a low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying on an azimuth plane whose origin is located +.>
Average radius of +.>
Distribution variance of->
The variance of the relative angle between the high-precision unmanned carriers and the low-precision unmanned carriers is represented by I, wherein I is an identity matrix, and the probability distribution of the azimuth plane is represented by the following formula:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 6.5) are as follows:
let the altitude angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be e, and the belief state of the low-precision flying j be
The belief state of the high-precision unmanned carrier k is
If the altitude information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, according to the altitude information e, the altitude direction information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j is defined as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying in the direction of the altitude whose origin lies in +.>
Average radius of +.>
Distribution variance of->
The variance of the relative angle between the high-precision unmanned carriers and the low-precision unmanned carriers is represented by I, wherein I is an identity matrix, and the probability distribution in the height direction is represented by the following formula:
as a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 7) are as follows:
step 7.1), for the low precision unmanned carrier j, based on its initial position estimate
Sampling n sample points s a ，1≤a≤n；
Step 7.2), calculate sample point s a At the time of sampling at probability distribution
Probability density q(s) a )；
Step 7.3), calculate sample point s a In K cooperative measurement messages eta (X j ) Probability density p at d (s a )；
Step 7.4), weight is allocated to each sample point, and the weight of the a sample point is the weight of the a sample point
In the formula, pi is a cumulative sign, and then the weight w is given to a Normalization is performed so that->
Step 7.5), estimating a new position mean value according to the weighted sample points
I.e. covariance->
The calculation formula is as follows:
step 7.6), repeating steps 7.1) to 7.5) until the position of the unmanned carrier j is estimated with low accuracy
Covariance->
Convergence, will converge +.>
As an optimized position estimate for the low-accuracy unmanned carrier j.
As a further optimization scheme of the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling, the detailed steps of the step 7) are as follows:
step 7. A), estimating the initial position of the low-precision unmanned carrier j as
The cooperative measurement message provided by the high-precision unmanned carrier for the low-precision unmanned carrier is eta (X j ) The position distribution of the low-precision unmanned carrier is optimized by a probability distribution multiplication method, and the calculation formula is as follows:
wherein K is the total number of cooperative measurement messages, and pi is a cumulative symbol;
step 7. B) due to the position estimate b (X j ) Cooperative measurement message eta (X j ) The formulas in step 7. A) are simply calculated by using normal distribution N (μ, Σ) approximation, and the normal distribution product calculation formula is as follows:
N(μ c ,Σ c )∝N(μ 1 ,Σ 1 )·N(μ 2 ,Σ 2 )
wherein the method comprises the steps of
Step 7. C), mu.is added c 、Σ c As an optimized position estimate for the low-accuracy unmanned carrier j.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
according to the invention, when unmanned carriers with different types and positioning precision are formed and fly, the unmanned carrier with higher positioning precision is used as a high-precision unmanned carrier, the unmanned carrier with lower positioning precision is used as a low-precision unmanned carrier, the positioning precision of the low-precision unmanned carrier is optimized by utilizing the reference position information of the high-precision unmanned carrier and the relative distance and angle information between the high-precision unmanned carrier and the low-precision unmanned carrier, and the positioning performance of the navigation system under complex terrain and electromagnetic interference environment is improved, so that the navigation system is very important for normal and stable operation of the cross-domain unmanned cluster in the complex environment, and has important military and economic values.
Drawings
FIG. 1 is a flow chart illustrating the principles of the present invention;
FIG. 2 is a messaging model constructed in accordance with the present invention;
FIG. 3 is a schematic diagram of a cluster co-navigation simulation;
FIG. 4 is a three-dimensional schematic diagram of the geometric distribution of a low-precision unmanned carrier obtained by relative ranging of the high-low unmanned carrier;
FIG. 5 is a two-dimensional schematic diagram of the geometric distribution of the low-precision unmanned carrier obtained by the relative ranging of the high-low unmanned carrier;
FIG. 6 is a three-dimensional schematic diagram of the geometric distribution of the low-precision unmanned carrier obtained by high-low unmanned carrier relative goniometry, ranging/goniometry;
FIG. 7 is a two-dimensional schematic of the geometric distribution of the low-precision unmanned carrier obtained by high-low unmanned carrier relative goniometry, ranging/goniometry;
FIG. 8 is a three-dimensional schematic diagram of low-precision unmanned carrier geometry obtained by high-low unmanned carrier relative ranging, ranging/angulation;
FIG. 9 is a two-dimensional schematic of low-precision unmanned carrier geometry obtained by high-low unmanned carrier relative ranging, ranging/angulation;
FIG. 10 is a schematic diagram of a simulation of a geometric distribution sampling algorithm of the present invention;
FIG. 11 is a graph comparing the positioning results of the same low-precision unmanned carrier using different collaborative navigation enhancement methods.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
this invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, the components are exaggerated for clarity.
The invention provides a cross-domain unmanned cluster collaborative navigation information fusion method based on a geometric distribution sampling assembly, which comprises the steps of taking an unmanned carrier with higher positioning precision as a high-precision unmanned carrier, taking the unmanned carrier with lower positioning precision as a low-precision unmanned carrier, and improving the positioning precision of the low-precision unmanned carrier by utilizing the reference position information of the high-precision unmanned carrier and the relative distance and angle information between the unmanned carrier and the low-precision unmanned carrier when unmanned carriers with different types and positioning precision are formed and fly; estimating respective positioning errors of the high-precision unmanned carrier and the low-precision unmanned carrier under the condition of given position observation of the high-precision unmanned carrier and the low-precision unmanned carrier; through a message transmission model, using geometrical distribution to describe the joint posterior distribution of the positions of the high-precision and low-precision unmanned carriers, converting the co-location problem of the low-precision unmanned carriers into an inference problem on a graph model, and calculating the co-location information provided by the high-precision unmanned carriers for the low-precision unmanned carriers according to the geometrical relation of the relative measurement information; based on a message passing algorithm, the co-location enhancement of the low-precision unmanned carrier is completed through a geometric probability distribution sampling algorithm or a probability distribution product algorithm. The invention can obviously improve the positioning performance of the low-precision unmanned carrier under the condition of less high-precision unmanned carriers, and is suitable for practical application. A schematic flow chart of the principle of the invention is shown in fig. 1. The messaging model established by the present invention is shown in fig. 2. The coordinated navigation condition of the unmanned carriers of the cluster is shown in figure 3.
A cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling comprises the following steps:
step 1), acquiring longitude, latitude and altitude of the cross-domain unmanned clusters required by co-location and relative measurement data among the cross-domain unmanned clusters, wherein the relative measurement data comprises a distance, an azimuth angle and an altitude angle.
And 2) converting navigation parameters of longitude, latitude and altitude of the cross-domain unmanned cluster into rectangular coordinates (x, y, z) in an earth coordinate system.
Step 3), estimating the positioning error of the cross-domain unmanned cluster according to the positioning type of the cross-domain unmanned cluster, and estimating the position covariance:
step 3.1), if the positioning type of the unmanned carrier is geometric positioning, the error equation set of navigation positioning is expressed as:
R-P＝G u ·X u
wherein R= (R) 1 r 2 … r n ) The geometric distance from the unmanned carrier to be positioned to the geometric positioning reference station m is more than or equal to 1 and less than or equal to n; p= (ρ) 1 ρ 2 … ρ n ) A measurement pseudo range from the unmanned carrier to be positioned to the geometric positioning reference station m;
wherein (h) m1 h m2 h m3 ) Cosine, X of three directions of vector of unmanned carrier to be positioned to geometric positioning reference station m ( m epsilon 1,2,3 … n) u ＝[δx δy δz δu t ]，[δx δy δz]To locate position fix number δu t Is a range error caused by clock error;
order the
The geometric error coefficients of the position of the unmanned carrier to be positioned are:
the variance of the geometric positioning range error is
The covariance Σ of its three-dimensional position p The estimated values of (2) are as follows:
step 3.2), if the positioning type of the unmanned carrier is Kalman filtering integrated navigation, according to a Kalman filtering position covariance matrix P k Estimating three-dimensional position error thereof, covariance Σ of three-dimensional position thereof p The estimated values of (2) are as follows:
wherein lambda is,
h is longitude, latitude, altitude, respectively, obtained by Kalman filtering>
Respectively Kalman filtering position covariance matrix P k The variance of the longitude position, the covariance of the latitude and longitude, the variance of the latitude and the variance of the altitude, R is the curvature radius of the earth;
step 3.3), if the positioning type of the unmanned carrier is the calculation formula positioning, as the calculation formula positioning error diverges along with time, the error delta lambda of the inertial navigation system in the longitudinal direction is estimated according to the drift parameter epsilon of the constant value of the inertial navigation gyro of the unmanned carrier and the inertial navigation working time t, and the calculation formula is as follows:
according to the radius R of the sphere of the earth e Setting a covariance sigma of the three-dimensional position of the unmanned carrier to be positioned p Matrix:
and 4) setting an accuracy dividing threshold tau according to the positioning error estimation of the unmanned carrier, setting the unmanned carrier with the position covariance estimation larger than tau as a low-accuracy unmanned carrier, and setting the unmanned carrier with the position covariance estimation smaller than or equal to tau as a high-accuracy unmanned carrier.
Step 5), obtaining probability distribution of unmanned carrier positions, and establishing position variable nodes in a message transfer model according to the mean value and covariance of the positions:
step 5.1), the coordinates of the ith unmanned carrier under the earth coordinate system are (x) i ,y i ,z i ) The position covariance estimate is
Approximating the position distribution of the unmanned carrier by using the multivariate normal distribution, the probability distribution of the positions of the ith unmanned carrier is:
wherein X is i Probability distribution for the i-th unmanned carrier position,
the position mean value of the ith unmanned carrier is obtained, and N is normal distribution in probability statistics;
step 5.2), in the graph model, using b (X i ) Beliefs indicating the location of the ith unmanned carrier itself:
step 6), traversing the relative measurement information of the high-precision unmanned carriers and the low-precision unmanned carriers according to the relative measurement information between the high-precision unmanned carriers and the low-precision unmanned carriers and the position estimation of each unmanned carrier, and establishing corresponding cooperative measurement information according to the type of the relative measurement information:
step 6.1), if the relative measurement information is distance information, establishing a distance measurement cooperative measurement message eta in a message transfer model based on a spherical distribution geometric model ρ ：
Let the distance measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be
The belief state of j for low-precision flight is +.>
The belief state of the high-precision unmanned carrier k is
If high precisionThe distance information is measured relatively between the unmanned carrier k and the low-precision unmanned carrier j according to the distance information rho k→j Defining the distance message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a spherical distribution, as shown in fig. 4 and 5, with the sphere center of the spherical distribution at
Average radius ρ k→j The sphere thickness is->
For the variance of the relative ranging between high and low precision unmanned carriers, I is an identity matrix, and the formula of the spherical probability distribution is as follows:
in the middle of
Representing the calculation vector +.>
The probability distribution is:
step 6.2) if the relative measurement information is line-of-sight angle information, establishing geometry in the message passing model based on the ray distributionObserving model component messages eta ξ ：
Let the angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be xi k→j ＝[θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
If the angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the angle information xi is measured according to k→j ＝[θ,e]Defining the angle message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
θ, e are the azimuth, altitude angle, and +.>
The position distribution of the low-precision unmanned carrier j is regarded as a probability distribution in the radial direction, and the end point of the radial distribution is positioned in +.>
Average radius of +.>
Distribution variance of->
The variance of the relative sight angle measurement between high and low precision unmanned carriers is represented by I, wherein I is an identity matrix, and the formula of the probability distribution of the rays is as follows:
in the middle of
Representing the calculation vector +.>
Is a binary norm of (2);
step 6.3), if the relative measurement information is ranging/line-of-sight angle information, establishing a geometrical observation model component message eta in the message transfer model based on vector distribution γ ：
Let the distance between the low-precision unmanned carrier j and the high-precision unmanned carrier k be gamma k→j ＝[ρ,θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is +.>
If the distance and the sight angle information are measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the distance and the sight angle information gamma are used for being based on k→j ＝[ρ,θ,e]Defining the distance and angle measurement information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution at the end of the vector, the start of which is located +.>
Average radius ρ, distribution variance +.>
The variance of the relative sight angle between high and low precision unmanned carriers is represented by the formula of the vector probability distribution, wherein I is an identity matrix:
step 6.4), if the relative measurement information is azimuth information, establishing a geometrical observation model component message eta in the message transfer model based on azimuth plane distribution θ ：
Let the azimuth angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be theta k→j The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
/>
If the azimuth angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the ranging angle information theta is used for measuring k→j Defining azimuth direction information transmitted between a high-precision unmanned carrier k and a low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying on an azimuth plane whose origin is located +.>
Average radius of +.>
Distribution variance of->
The variance of the relative angle between the high-precision unmanned carriers and the low-precision unmanned carriers is represented by I, wherein I is an identity matrix, and the probability distribution of the azimuth plane is represented by the following formula:
step 6.5), if the relative measurement information is the altitude angle information, establishing a geometrical observation model assembly message eta in the message transfer model based on the upward-looking surface distribution model e ：
Let the altitude angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be e, and the belief state of the low-precision flying j be
The belief state of the high-precision unmanned carrier k is
If the altitude information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, according to the altitude information e, the altitude direction information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j is defined as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying in the direction of the altitude whose origin lies in +.>
Average radius of +.>
Distribution variance of->
Is of high and low precisionThe variance of the relative angle between unmanned carriers is calculated, I is an identity matrix, and the formula of the probability distribution in the height direction is as follows:
and 7) fusing the positions of the high-precision unmanned carrier and the low-precision unmanned carrier with the relative measurement information through a geometric probability distribution sampling and probability distribution product algorithm, and obtaining the precise positions of the low-precision unmanned carrier.
The step 7) has two embodiments. The first detailed procedure is as follows:
step 7.1), for the low precision unmanned carrier j, based on its initial position estimate
Sampling n sample points s a ，1≤a≤n；
Step 7.2), calculate sample point s a At the time of sampling at probability distribution
Probability density q(s) a )；
Step 7.3), calculate sample point s a In K cooperative measurement messages eta (X j ) Probability density p at d (s a )；
Step 7.4), weight is allocated to each sample point, and the weight of the a sample point is the weight of the a sample point
In the formula, pi is a cumulative sign, and then the weight w is given to a Normalization is performed so that->
As shown in fig. 7;
step 7.5), estimating a new position mean value according to the weighted sample points
I.e. covariance->
The calculation formula is as follows:
step 7.6), repeating steps 7.1) to 7.5) until the position of the unmanned carrier j is estimated with low accuracy
Covariance->
Convergence, will converge +.>
As an optimized position estimate for the low-accuracy unmanned carrier j.
The second detailed procedure is as follows:
step 7. A), estimating the initial position of the low-precision unmanned carrier j as
The cooperative measurement message provided by the high-precision unmanned carrier for the low-precision unmanned carrier is eta (X j ) The position distribution of the low-precision unmanned carrier is optimized by a probability distribution multiplication method, and the calculation formula is as follows:
wherein K is the total number of cooperative measurement messages, and pi is a cumulative symbol;
step 7. B) due to the position estimate b (X j ) Cooperative measurement message eta (X j ) The formulas in step 7. A) are simply calculated by using normal distribution N (μ, Σ) approximation, and the normal distribution product calculation formula is as follows:
N(μ c ,Σ c )∝N(μ 1 ,Σ 1 )·N(μ 2 ,Σ 2 )
wherein the method comprises the steps of
Step 7. C), mu.is added c 、Σ c As an optimized position estimate for the low-accuracy unmanned carrier j.
FIG. 10 is a schematic diagram of a geometric distribution sampling algorithm according to the present invention, wherein w is the weight of each sampling particle.
The positioning results of the same low-precision unmanned carrier using different collaborative navigation enhancement methods are shown in fig. 11, and it can be seen from the figure that the positioning precision is significantly improved after the method of the invention is used.
According to the cross-domain unmanned cluster collaborative navigation information fusion method based on the geometric distribution sampling assembly, geometric distribution of low-precision unmanned carrier positions is obtained through various relative measurement information such as distance, line-of-sight angle, distance/line-of-sight angle, altitude angle and azimuth angle among unmanned carriers, then sampling is carried out near the low-precision unmanned carrier approximate positions, and more accurate positioning is calculated according to the weight of sampling points on the geometric distribution. As a plurality of observables of the distance, the sight angle, the distance/the sight angle, the altitude angle and the azimuth angle among unmanned carriers can be applied, the method is more suitable for the collaborative navigation condition of a plurality of relative measurement information existing in the cluster compared with the traditional method. Compared with a sigma-point method, the method has smaller positioning error and is more suitable for the clustered unmanned carrier working in a complex three-dimensional environment.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.
Claims (10)
1. The cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling is characterized by comprising the following steps of:
step 1), acquiring longitude, latitude and altitude of a cross-domain unmanned cluster required by co-location and relative measurement data among the cross-domain unmanned clusters, wherein the relative measurement data comprises a distance, an azimuth angle and an altitude angle;
step 2), converting navigation parameters of longitude, latitude and altitude of the cross-domain unmanned cluster into rectangular coordinates (x, y, z) in an earth coordinate system;
step 3), estimating the positioning error of the cross-domain unmanned cluster according to the positioning type of the cross-domain unmanned cluster, and estimating the position covariance;
step 4), setting an accuracy dividing threshold tau according to the positioning error estimation of the unmanned carrier, setting the unmanned carrier with the position covariance estimation larger than tau as a low-accuracy unmanned carrier, and setting the unmanned carrier with the position covariance estimation smaller than or equal to tau as a high-accuracy unmanned carrier;
step 5), obtaining probability distribution of unmanned carrier positions, and establishing position variable nodes in a message transfer model according to the mean value and covariance of the positions;
step 6), traversing the relative measurement information of the high-precision unmanned carriers and the low-precision unmanned carriers according to the relative measurement information between the high-precision unmanned carriers and the low-precision unmanned carriers and the position estimation of each unmanned carrier, and establishing corresponding cooperative measurement information according to the type of the relative measurement information:
step 6.1), if the relative measurement information is distance information, establishing a distance measurement cooperative measurement message eta in a message transfer model based on a spherical distribution geometric model ρ ；
Step 6.2), if the relative measurement information is the line of sight information, establishing a geometrical observation model component message eta in the message transfer model based on the ray distribution ξ ；
Step 6.3), if the relative measurement information is ranging/line-of-sight angle information, establishing a geometrical observation model component message eta in the message transfer model based on vector distribution γ ；
Step 6.4), if the relative measurement information is azimuth information, establishing a geometrical observation model component message eta in the message transfer model based on azimuth plane distribution θ ；
Step 6.5), if the relative measurement information is the altitude angle information, establishing a geometrical observation model assembly message eta in the message transfer model based on the upward-looking surface distribution model e ；
And 7) fusing the positions of the high-precision unmanned carrier and the low-precision unmanned carrier with the relative measurement information through a geometric probability distribution sampling and probability distribution product algorithm, and obtaining the precise positions of the low-precision unmanned carrier.
2. The geometric distribution sampling-based cross-domain unmanned cluster collaborative navigation information fusion method according to claim 1, wherein the specific steps of the step 3) are as follows:
step 3.1), if the positioning type of the unmanned carrier is geometric positioning, the error equation set of navigation positioning is expressed as:
R-P＝G u ·X u
wherein R= (R) 1 r 2 … r n ) The geometric distance from the unmanned carrier to be positioned to the geometric positioning reference station m is more than or equal to 1 and less than or equal to n; p= (ρ) 1 ρ 2 ... ρ n ) A measurement pseudo range from the unmanned carrier to be positioned to the geometric positioning reference station m;
wherein (h) m1 h m2 h m3 ) Cosine, X of three directions from the unmanned carrier to be positioned to the m vector of the geometric positioning reference station u ＝[δx δy δz δu t ]，[δx δy δz]To locate position fix number δu t Is a range error caused by clock error;
order the
The geometric error coefficients of the position of the unmanned carrier to be positioned are:
the variance of the geometric positioning range error is
The covariance Σ of its three-dimensional position p The estimated values of (2) are as follows:
step 3.2), if the positioning type of the unmanned carrier is Kalman filtering integrated navigation, according to a Kalman filtering position covariance matrix P k Estimating three-dimensional position error thereof, covariance Σ of three-dimensional position thereof p The estimated values of (2) are as follows:
wherein lambda is,
h is longitude, latitude, altitude, respectively, obtained by Kalman filtering>
Respectively Kalman filtering position covariance matrix P k The variance of longitude in (1), the covariance of latitude and longitude, the variance of latitude and the variance of altitude, R is the curvature radius of the earth;
step 3.3), if the positioning type of the unmanned carrier is the calculation formula positioning, as the calculation formula positioning error diverges along with time, the error delta lambda of the inertial navigation system in the longitudinal direction is estimated according to the drift parameter epsilon of the constant value of the inertial navigation gyro of the unmanned carrier and the inertial navigation working time t, and the calculation formula is as follows:
according to the radius R of the sphere of the earth e Setting a covariance sigma of the three-dimensional position of the unmanned carrier to be positioned p Matrix:
3. the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 2, wherein the detailed steps of step 5) are as follows:
step 5.1), the coordinates of the ith unmanned carrier under the earth coordinate system are (x) i ,y i ,z i ) The position covariance estimate is
Approximating the position distribution of the unmanned carrier by using the multivariate normal distribution, the probability distribution of the positions of the ith unmanned carrier is:
wherein X is i Probability distribution for the i-th unmanned carrier position,
the position mean value of the ith unmanned carrier is obtained, and N is normal distribution in probability statistics;
step 5.2), in the graph model, using b (X i ) Belief status indicating the position of the ith unmanned carrier itself:
4. the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 3, wherein the detailed steps of the step 6.1) are as follows:
let the distance measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be ρ k→j The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is +.>
If the distance information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the distance information rho is used for measuring the distance information k→j Defining the distance message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a spherical distribution with the sphere center at +.>
Average radius ρ k→j The sphere thickness is->
For the variance of the relative ranging between high and low precision unmanned carriers, I is an identity matrix, and the formula of the spherical probability distribution is as follows:
in the middle of
Representing the calculation vector +.>
The probability distribution is:
5. the geometric distribution sampling-based cross-domain unmanned cluster collaborative navigation information fusion method according to claim 4, wherein the specific process of the step 6.2) is as follows:
let the angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be xi k→j ＝[θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
If the angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the angle information xi is measured according to k→j ＝[θ,e]Defining the angle message transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
θ, e are the azimuth, altitude angle, and +.>
The position distribution of the low-precision unmanned carrier j is regarded as a probability distribution in the direction of the rays whose end points are located +.>
Average radius of +.>
Distribution variance of->
The variance of the relative sight angle measurement between high and low precision unmanned carriers is represented by I, wherein I is an identity matrix, and the formula of the probability distribution of the rays is as follows:
in the middle of
Representing the calculation vector +.>
Is a binary norm of (c).
6. The cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 5, wherein the detailed steps of step 6.3) are as follows:
let the distance between the low-precision unmanned carrier j and the high-precision unmanned carrier k be gamma k→j ＝[ρ,θ,e]The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is
If the distance and the sight angle information are measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the distance and the sight angle information gamma are used for being based on k→j ＝[ρ,θ,e]Defining the distance and angle measurement information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution at the end of the vector whose origin is at +.>
Average radius ρ, distribution variance +.>
The variance of the relative sight angle between high and low precision unmanned carriers is represented by the formula of the vector probability distribution, wherein I is an identity matrix:
7. the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 6, wherein the detailed steps of step 6.4) are as follows:
let the azimuth angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be theta k→j The belief state of j for low-precision flight is
The belief state of the high-precision unmanned carrier k is +.>
If the azimuth angle information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, the ranging angle information theta is used for measuring k→j Defining azimuth direction information transmitted between a high-precision unmanned carrier k and a low-precision unmanned carrier j as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying on an azimuth plane whose origin is located +.>
Average radius of +.>
Distribution variance of->
The variance of the relative angle between the high-precision unmanned carriers and the low-precision unmanned carriers is represented by I, wherein I is an identity matrix, and the probability distribution of the azimuth plane is represented by the following formula:
8. the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 7, wherein the detailed steps of step 6.5) are as follows:
let the altitude angle measurement data between the low-precision unmanned carrier j and the high-precision unmanned carrier k be e, and the belief state of the low-precision flying j be
The belief state of the high-precision unmanned carrier k is +.>
If the altitude information is measured relatively between the high-precision unmanned carrier k and the low-precision unmanned carrier j, according to the altitude information e, the altitude direction information transmitted between the high-precision unmanned carrier k and the low-precision unmanned carrier j is defined as
The message regards the position distribution of the low-precision unmanned carrier j as a probability distribution lying in the direction of the altitude whose origin lies in +.>
Average radius of +.>
The distribution variance is
The variance of the relative angle between the high-precision unmanned carriers and the low-precision unmanned carriers is represented by I, wherein I is an identity matrix, and the probability distribution in the height direction is represented by the following formula:
9. the cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 8, wherein the detailed steps of the step 7) are as follows:
step 7.1), for the low precision unmanned carrier j, based on its initial position estimate
Sampling n sample points s a ，1≤a≤n；
Step 7.2), calculate sample point s a At the time of sampling at probability distribution
Probability density q(s) a )；
Step 7.3), calculate sample point s a In K cooperative measurement messages eta (X j ) Probability density p at d (s a )；
Step 7.4), weight is allocated to each sample point, and the weight of the a sample point is the weight of the a sample point
In the formula, pi is a cumulative sign, and then the weight w is given to a Normalization is performed so that->
Step 7.5), estimating new according to the weighted sample pointsPosition average of (2)
I.e. covariance->
The calculation formula is as follows:
step 7.6), repeating steps 7.1) to 7.5) until the position of the unmanned carrier j is estimated with low accuracy
Covariance->
Convergence, will converge +.>
As an optimized position estimate for the low-accuracy unmanned carrier j.
10. The cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling according to claim 8, wherein the detailed steps of the step 7) are as follows:
step 7. A), estimating the initial position of the low-precision unmanned carrier j as
The cooperative measurement message provided by the high-precision unmanned carrier for the low-precision unmanned carrier is eta (X j ) For low precision by probability distribution multiplicationThe position distribution of the unmanned carrier is optimized, and the calculation formula is as follows:
wherein K is the total number of cooperative measurement messages, and pi is a cumulative symbol;
step 7. B) due to the position estimate b (X j ) Cooperative measurement message eta (X j ) The formulas in step 7. A) are simply calculated by using normal distribution N (μ, Σ) approximation, and the normal distribution product calculation formula is as follows:
N(μ c ,Σ c )∝N(μ 1 ,Σ 1 )·N(μ 2 ,Σ 2 )
wherein the method comprises the steps of
Step 7. C), mu.is added c 、Σ c As an optimized position estimate for the low-accuracy unmanned carrier j.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310196450.1A CN116401618B (en) | 2023-03-03 | 2023-03-03 | Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310196450.1A CN116401618B (en) | 2023-03-03 | 2023-03-03 | Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116401618A true CN116401618A (en) | 2023-07-07 |
| CN116401618B CN116401618B (en) | 2023-12-01 |
Family
ID=87008227
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310196450.1A Active CN116401618B (en) | 2023-03-03 | 2023-03-03 | Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116401618B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090212995A1 (en) * | 2007-12-20 | 2009-08-27 | Shunguang Wu | Distributed iterative multimodal sensor fusion method for improved collaborative localization and navigation |
| WO2011053389A1 (en) * | 2009-10-29 | 2011-05-05 | Tele Atlas North America Inc. | Methods and apparatuses for determining a geometric feature of a navigable feature |
| CN106441300A (en) * | 2016-09-08 | 2017-02-22 | 哈尔滨工程大学 | Self-adaptive collaborative navigation and filtering method |
| EP3267152A1 (en) * | 2016-07-05 | 2018-01-10 | The Boeing Company | Navigation aids for unmanned aerial systems in a gps-denied environment |
| CN109901108A (en) * | 2019-03-19 | 2019-06-18 | 南京航空航天大学 | A kind of formation unmanned plane co-located method based on posteriority linearisation belief propagation |
| CN110954092A (en) * | 2019-11-28 | 2020-04-03 | 上海航天控制技术研究所 | Collaborative navigation method based on relative measurement information assistance |
| CN112698664A (en) * | 2020-12-11 | 2021-04-23 | 南京航空航天大学 | Sight line sector dynamic estimation method for unmanned cluster collaborative navigation optimization |
| CN113175931A (en) * | 2021-04-02 | 2021-07-27 | 上海机电工程研究所 | Cluster networking collaborative navigation method and system based on constraint Kalman filtering |
| CN113310489A (en) * | 2021-03-18 | 2021-08-27 | 南京航空航天大学 | Unmanned cluster collaborative navigation optimization method based on mutual observation virtual reference domain |
| CN114199248A (en) * | 2021-11-30 | 2022-03-18 | 哈尔滨工程大学 | AUV (autonomous underwater vehicle) cooperative positioning method for optimizing ANFIS (artificial neural field of view) based on mixed element heuristic algorithm |
| WO2022073376A1 (en) * | 2020-10-10 | 2022-04-14 | 东南大学 | Self-adaptive moving base rotation modulation fine alignment method based on multiple fading factors |
| CN114485645A (en) * | 2022-01-27 | 2022-05-13 | 浙江大学 | UUV cluster cooperative positioning system and method based on ranging and information interaction |
| CN114754772A (en) * | 2022-03-18 | 2022-07-15 | 上海机电工程研究所 | Distributed cluster collaborative navigation system and method based on data link networking ranging |
| WO2023023936A1 (en) * | 2021-08-24 | 2023-03-02 | 华为技术有限公司 | Positioning method and positioning apparatus |
-
2023
- 2023-03-03 CN CN202310196450.1A patent/CN116401618B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090212995A1 (en) * | 2007-12-20 | 2009-08-27 | Shunguang Wu | Distributed iterative multimodal sensor fusion method for improved collaborative localization and navigation |
| WO2011053389A1 (en) * | 2009-10-29 | 2011-05-05 | Tele Atlas North America Inc. | Methods and apparatuses for determining a geometric feature of a navigable feature |
| EP3267152A1 (en) * | 2016-07-05 | 2018-01-10 | The Boeing Company | Navigation aids for unmanned aerial systems in a gps-denied environment |
| CN106441300A (en) * | 2016-09-08 | 2017-02-22 | 哈尔滨工程大学 | Self-adaptive collaborative navigation and filtering method |
| CN109901108A (en) * | 2019-03-19 | 2019-06-18 | 南京航空航天大学 | A kind of formation unmanned plane co-located method based on posteriority linearisation belief propagation |
| CN110954092A (en) * | 2019-11-28 | 2020-04-03 | 上海航天控制技术研究所 | Collaborative navigation method based on relative measurement information assistance |
| WO2022073376A1 (en) * | 2020-10-10 | 2022-04-14 | 东南大学 | Self-adaptive moving base rotation modulation fine alignment method based on multiple fading factors |
| CN112698664A (en) * | 2020-12-11 | 2021-04-23 | 南京航空航天大学 | Sight line sector dynamic estimation method for unmanned cluster collaborative navigation optimization |
| CN113310489A (en) * | 2021-03-18 | 2021-08-27 | 南京航空航天大学 | Unmanned cluster collaborative navigation optimization method based on mutual observation virtual reference domain |
| CN113175931A (en) * | 2021-04-02 | 2021-07-27 | 上海机电工程研究所 | Cluster networking collaborative navigation method and system based on constraint Kalman filtering |
| WO2023023936A1 (en) * | 2021-08-24 | 2023-03-02 | 华为技术有限公司 | Positioning method and positioning apparatus |
| CN114199248A (en) * | 2021-11-30 | 2022-03-18 | 哈尔滨工程大学 | AUV (autonomous underwater vehicle) cooperative positioning method for optimizing ANFIS (artificial neural field of view) based on mixed element heuristic algorithm |
| CN114485645A (en) * | 2022-01-27 | 2022-05-13 | 浙江大学 | UUV cluster cooperative positioning system and method based on ranging and information interaction |
| CN114754772A (en) * | 2022-03-18 | 2022-07-15 | 上海机电工程研究所 | Distributed cluster collaborative navigation system and method based on data link networking ranging |
Non-Patent Citations (3)
| Title |
|---|
| "Cooperative positioning algorithm of swarm UAVs based on posterior linearization belief propagation", 《IEEE ACCESS》 * |
| MINGXING CHEN: "Cooperative navigation of unmanned aerial vehicle swarm based on cooperative dilution of precision", 《ROBOTICS AND INERTIAL INTEGRATED NAVIGATION TECHNO-RESEARCH ARTICLE》 * |
| 赵惟成: "基于异源视距分析的多域协同导航增强方法", 《第十三届中国卫星导航年会论文集》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116401618B (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bergman et al. | Terrain navigation using Bayesian statistics | |
| CN105301601B (en) | A kind of GNSS ionosphere delay three-dimensional modeling methods suitable for Global Regional | |
| CN107315171B (en) | Radar networking target state and system error joint estimation algorithm | |
| CN105044667A (en) | Double-satellite tracking method, device and system for moving target | |
| CN111765879A (en) | Three-dimensional electronic compass device and practical calibration method | |
| Elgamoudi et al. | A survey for recent techniques and algorithms of geolocation and target tracking in wireless and satellite systems | |
| CN111199281B (en) | Short wave single station direct positioning deviation compensation method based on geographical coordinate airspace position spectrum | |
| CN111428369A (en) | Method for calculating confidence of space target collision early warning result | |
| Pagoti et al. | Development and performance evaluation of Correntropy Kalman Filter for improved accuracy of GPS position estimation | |
| CN116401618B (en) | Cross-domain unmanned cluster collaborative navigation information fusion method based on geometric distribution sampling | |
| CN110728026B (en) | Terminal trajectory target passive tracking method based on angular velocity measurement | |
| CN116047495B (en) | State transformation fusion filtering tracking method for three-coordinate radar | |
| Kauffman et al. | Simulation study of UWB-OFDM SAR for navigation with INS integration | |
| CN117031519A (en) | Multi-target three-dimensional co-location method and system | |
| CN115560757B (en) | Unmanned aerial vehicle direct positioning correction method based on neural network under random attitude error condition | |
| CN116736219A (en) | Passive TDOA-FDOA combined positioning and optimizing station arrangement method based on improved particle swarm algorithm | |
| Li et al. | A novel single satellite passive location method based on one-dimensional cosine angle and Doppler rate of changing | |
| CN114763998B (en) | Unknown environment parallel navigation method and system based on micro radar array | |
| Jiexin et al. | Combination of land-based and satellite-based OTH geolocations using differentiable exact penalty method | |
| CN113310489B (en) | Unmanned cluster collaborative navigation optimization method based on mutual observation virtual reference domain | |
| CN114063647A (en) | Multi-unmanned aerial vehicle mutual positioning method based on distance measurement | |
| Shen et al. | Passive single satellite geolocation of ground-based EMI sources | |
| CN114608578B (en) | Weighted uncertainty unmanned aerial vehicle cluster collaborative navigation method | |
| CN116182865A (en) | Heterogeneous cluster collaborative navigation optimization method based on hybrid linearization message transfer | |
| CN113359837B (en) | Method for tracking moving source by using single unmanned aerial vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |