CN116358544A - Method and system for correcting inertial navigation error based on acoustic feature matching positioning - Google Patents
Method and system for correcting inertial navigation error based on acoustic feature matching positioning Download PDFInfo
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- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
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Abstract
The invention discloses a method and a system for correcting inertial navigation errors based on acoustic feature matching positioning, wherein the method comprises the following steps: according to the underwater sound propagation model of the ocean environment and the environmental acoustic feature information base, an environmental acoustic feature information vector base is established; based on the environmental acoustic feature information vector library, acoustic feature information matching and positioning are carried out, and position information of the underwater unmanned aircraft is obtained; and selecting an acoustic feature matching positioning system as an auxiliary navigation system, forming a combined navigation system with an inertial navigation system, selecting classical Kalman filtering for information fusion, adopting an indirect estimation method, and correcting navigation parameters of the inertial navigation system by using an open loop correction method based on the obtained position information of the underwater unmanned aircraft. According to the invention, the floating water surface on the underwater vehicle or the active emission of the sound wave signal is not needed, the sound source information of the existing beacons on the water surface or the underwater is only needed to be passively received, and the error correction of the SINS can be realized by adopting a single hydrophone.
Description
Technical Field
The invention relates to the technical field of positioning, in particular to a method for correcting inertial navigation errors based on acoustic feature matching positioning.
Background
Autonomous navigation positioning capability is essential to submarines in order to ensure long-term submersion of submarines under water. The navigation instrument of the submarine is comparatively more, but mainly depends on the inertia navigation system, the passivity and autonomy of the inertia navigation, creates conditions for realizing the autonomous navigation and positioning of the submarine, and becomes the core navigation equipment of the submarine. However, the main disadvantage of inertial navigation systems is that positioning errors increase with time and the long-term high-precision navigation positioning requirements cannot be met. In general, in order to suppress an increase in position error of a navigation system and improve accuracy of long-term navigation, it is necessary to periodically calibrate an inertial navigation system. Therefore, the development of the key technical research of autonomous underwater integrated navigation positioning is of great significance to the construction of an autonomous, steady and high-precision underwater navigation system, and is also an important direction of the development of the underwater integrated navigation positioning. In engineering practical application, other navigation systems are often used as auxiliary navigation systems, and sub-navigation systems are used for correcting errors generated by accumulation of SINS long-voyage time; or the craft floats up to the water surface regularly, and the GNSS is adopted to correct SINS errors.
At present, a common single navigation system cannot meet the severe requirements of an underwater complex environment on navigation positioning of an aircraft, and the combined navigation positioning based on multiple sensors is a future development direction. Currently, SINS is generally used as a main sensor when designing integrated navigation systems. However, SINS inevitably has the disadvantage that navigation errors gradually accumulate as time becomes longer, and the navigation accuracy is low under long voyage conditions.
Based on the above, in practical engineering applications, other navigation systems are often used as auxiliary navigation systems to correct errors generated by accumulating the long voyage of the SINS with time. The most common navigation subsystems in integrated navigation are SINS, doppler log (DVL), GNSS, underwater acoustic positioning systems such as Ultra Short Baseline (USBL) and Long Baseline (LBL), etc. In the SINS and DVL integrated navigation positioning method, the DVL measures the carrier ground speed by transmitting sound waves to the sea bottom, and although high-precision speed information can be measured, the defect that errors accumulate along with time is also caused; in the SINS and GNSS combined navigation positioning method, UUV needs to float upwards to be close to the water surface, and the risk of exposure concealment exists; in the combined navigation positioning method of the SINS and the underwater acoustic positioning system, when a plurality of UUV exist in the same sea area, acoustic signal exchange for positioning and data transmission becomes complex, and signals are always required to be actively transmitted, the SINS and the underwater acoustic positioning system can be exposed, and the concealment is lost, wherein in an LBL system, a plurality of beacons are required to determine the position of the SINS; in the USBL system, a plurality of array elements are needed to realize positioning, and the system is large in size and is not suitable for a small UUV. Therefore, a method is needed for timely correction of inertial navigation systems without requiring the UUV to approach the water surface. The acoustic feature matching positioning (Acoustic feature matching localization system, AFMLS) adopted by the invention assists navigation, has the characteristics of higher precision, no need of being exposed on the water surface, no external radiation and the like, and can finally solve the problem of concealment of UUV.
In the aspect of underwater acoustic matching positioning, the traditional underwater acoustic signal matching field positioning method is based on the fact that sound waves are plane waves and isotropic sound fields, a rich array signal processing method is developed, and the processing gain is improved by using a matched filtering technology. However, in an actual marine environment, due to the non-uniformity of sea water and the influence of sea surface and seabed boundaries, the actual sound field deviates significantly from the plane wave assumption, and it is difficult to accurately position the underwater target by the conventional matching field positioning method.
Disclosure of Invention
Aiming at the problem that the error of an underwater UUV inertial navigation system is accumulated and increased along with time and is difficult to independently work for a long time, the invention provides a method and a system for correcting the inertial navigation error based on acoustic feature matching positioning, which are used for solving the technical problems.
The invention discloses a method for correcting inertial navigation errors based on acoustic feature matching positioning, which comprises the following steps:
step 1: according to the underwater sound propagation model of the ocean environment and the environmental acoustic feature information base, an environmental acoustic feature information vector base is established;
step 2: based on the environmental acoustic feature information vector library, acoustic feature information matching and positioning are carried out, and position information of the underwater unmanned aircraft is obtained;
step 3: and selecting an acoustic feature matching positioning system as an auxiliary navigation system, forming a combined navigation system with an inertial navigation system, selecting classical Kalman filtering for information fusion, adopting an indirect estimation method, and correcting navigation parameters of the inertial navigation system by using an open loop correction method based on the obtained position information of the underwater unmanned aircraft.
Further, the step 1 includes:
placing a test sound source near the underwater unmanned aircraft to obtain prior information of sound source radiation signal propagation based on the current environment;
according to the obtained prior information of the sound source radiation signal propagation of the current environment and the channel characteristic parameters, selecting a water sound propagation model suitable for the marine environment, and inputting known marine environment parameters into the water sound propagation model; wherein the channel characteristic parameters comprise sound velocity profile, environment parameters and underwater sound propagation characteristics; the marine environmental parameters comprise temperature, salinity and depth;
according to the position change of the hydrophone of the underwater unmanned aircraft, the arrival structure of the received signals is different, namely, the relative arrival time delay and the amplitude of each intrinsic sound ray are different, and an environmental acoustic characteristic information base is constructed;
setting a search grid of a hydrophone in a possible occurrence area of a beacon sound source, wherein each grid point is used as an assumed beacon sound source position, adopting priori information, obtaining the arrival time delay and amplitude of an intrinsic sound ray of a test sound source (r, z) reaching the receiving hydrophone through calculation by utilizing a sound ray theoretical model, and establishing an eigenvoice arrival time delay and amplitude characteristic library of the beacon sound source reaching the receiving hydrophone as an environment acoustic characteristic information vector library; the a priori information includes environmental parameters and hydrophone position.
Further, the step 2 includes:
step 21: searching as many intrinsic sound rays as possible, calculating all the additional information of the intrinsic sound rays, then combining all the additional information of the intrinsic sound rays together, and decomposing characteristic parameters of the intrinsic sound rays from actual received signals to serve as matching information; wherein the intrinsic sound ray additional information comprises propagation attenuation and propagation delay; the characteristic parameters comprise an arrival angle and a relative arrival time delay;
step 22: performing acoustic characteristic information matching operation according to the known intrinsic sound ray arrival structure diagram, extracting position characteristic information, and finally obtaining a position relative estimated value of the beacon relative to the underwater unmanned vehicle;
step 23: and according to the relative estimated value of the position of the beacon relative to the underwater unmanned vehicle and the position information of the beacon in the acoustic signal, the position information of the underwater unmanned vehicle can be calculated.
Further, the step 21 includes:
assuming that N eigen sound rays reach the receiving hydrophone, the channel transfer function from the beacon sound source to the receiving hydrophone is expressed as:
wherein z is s 、z r And r respectively represent the sound source depth, the receiver depth and the distance between the receiver and the sound source; g i Is the amplitude of the ith eigen-acoustic line, n i Is the propagation delay of the ith intrinsic sound ray;
the received signal at the receiving hydrophone is represented as
Where x (n) represents a received signal, s (n) represents a sound source signal, and e (n) represents noise;
and obtaining the arrival time of the intrinsic sound ray based on the received signal at the receiving hydrophone, namely finishing the estimation of the relative time delay of the intrinsic sound ray.
Further, the step 22 includes:
and directly correlating the autocorrelation function of the environmental acoustic characteristic information with the autocorrelation function of the hydrophone signal by using the environmental acoustic characteristic information vector library, so that the sound source position can be estimated.
Further, the step 22 specifically includes:
band-pass filtering the received signal of hydrophone and calculating its autocorrelation function R xx ；
The prior information is adopted, and the acoustic line theoretical model is utilized to obtain the arrival time delay and the amplitude of the intrinsic acoustic line of the test sound source (r, z) reaching the receiving hydrophone through calculation; the prior information includes environmental parameters and hydrophone positions;
autocorrelation function R of environmental acoustic characteristic signal rplc Autocorrelation function R of (R, z) and hydrophone signals xx Performing correlation processing to obtain a correlation coefficient rho (r, z);
searching is performed in the expected beacon sound source position area, and a fuzzy plane of the correlation coefficient rho (r, z) is constructed, wherein the peak position of the fuzzy plane is estimated as the relative position of the beacon sound source.
Further, assuming that the signal and noise are uncorrelated, the autocorrelation function of the received signal x (n) is:
wherein R is ee As an autocorrelation function of noise e (n), R ss Is an autocorrelation function of the sound source signal s (n).
Further, the step 3 includes:
step 31: the position information of the underwater unmanned aircraft can be obtained through the calculation in the step 2, the position information is compared with the position information of the underwater unmanned aircraft corrected at the last moment, the possible jump or frame loss condition of the output data is judged, when the output position information of the acoustic feature matching positioning system is in the threshold range, the positioning result data of the acoustic feature matching positioning system is normally available, and the step 32 is entered; otherwise, continuing to carry out navigation positioning by means of the inertial navigation system, and directly outputting navigation information of the inertial navigation system;
step 32: the Kalman filter takes the course, the gesture and the position error of the inertial navigation system as state estimation values, the position information provided by the acoustic feature matching positioning system is taken as measurement information, the error amount of the acoustic feature matching positioning system is estimated through the Kalman filter and then fed back to the inertial navigation system to correct the navigation result of the inertial navigation system, meanwhile, the navigation information of the acoustic feature matching positioning system can be smoothed, and finally the corrected reliable navigation information of the combined navigation system is obtained.
Further, in the step 32:
the system state equation is expressed as:
wherein X is SINS F is a state variable of the inertial navigation system SINS Is a state matrix of an inertial navigation system, W SINS/AFMLS State noise for inertial navigation system;
and selecting the difference between the position information calculated by the inertial navigation system and the acoustic feature matching and positioning system as an observed value, and expressing the position information of the inertial navigation system as the sum of a true value and an error value:
similarly, acoustic feature matching localization system location information can be expressed as:
the measurement equation for the system is expressed as:
wherein eta AFMLS Is the measurement noise of the acoustic feature matching positioning system;
the measurement matrix is expressed as:
H LBL ＝[0 3×6 I 3×3 0 3×6 ]
the acoustic array is arranged on a carrier of the unmanned underwater vehicle, and the position of the unmanned underwater vehicle in a rectangular geodetic rectangular coordinate system is (X) UUV ,Y UUV ,Z UUV ) The position in longitude and latitude coordinates is (L UUV ,λ UUV ,Z UUV ) The method comprises the steps of carrying out a first treatment on the surface of the Setting an inertial navigation system coordinate reference, and calibrating the acoustic array coordinate system and the carrier coordinate system reference to be consistent;
according to the position information (X) of the earth rectangular coordinate system carried in the beacon sound source information ob ,Y ob ,Z ob ) Or longitude and latitude coordinate information (lambda) ob ,L ob ,Z ob ) And the position (x) of the beacon sound source relative to the underwater unmanned vehicle, which is obtained by the acoustic feature matching positioning system AFMLS ,y AFMLS ,z AFMLS ) The position coordinate (X) of the ground rectangular coordinate system of the underwater unmanned aerial vehicle, which is obtained through acoustic feature matching, positioning and resolving, can be obtained AFMLS ,Y AFMLS ,Z AFMLS ) Or longitude and latitude coordinates (L AFMLS ,λ AFMLS ,Z AFMLS ) The following formula is shown:
(X AFMLS ,Y AFMLS ,Z AFMLS )＝(X ob ,Y ob ,Z ob )+(x AFMLS ,y AFMLS ,z AFMLS )
the inertial navigation system outputs an underwater unmanned vehicle coordinate representation (X) SINS ,Y SINS ,Z SINS ) Or (L) SINS ,λ SINS ,Z SINS ) The method comprises the steps of carrying out a first treatment on the surface of the The kalman filter outputs a combined navigation position coordinate representation (X kalman ,Y kalman ,Z kalman ) Or (L) kalman ,λ kalman ,Z kalman )；
When the inertial navigation system needs calibration, the inertial navigation system error (δx of the underwater unmanned vehicle SINS ,δY SINS ,δZ SINS ) Or (delta L) SINS ,δλ SINS ,δZ SINS ) Error in the geodetic rectangular coordinate system:
(δX SINS ,δY SINS ,δZ SINS )
＝(X SINS ,Y SINS ,Z SINS )-(X kalman ,Y kalman ,Z kalman )
errors of inertial navigation system in longitude and latitude geographic coordinate system:
(δL SINS ,δλ SINS ,δZ SINS )
＝(L SINS ,λ SINS ,Z SINS )-(L kalman ,λ kalman ,Z kalman )
the error is used for correcting the position information of the inertial navigation system and resetting the navigation parameters.
The invention also discloses a system for correcting inertial navigation errors based on acoustic feature matching positioning, which comprises:
the acoustic feature matching and positioning system is used for outputting the position information of the underwater unmanned aircraft;
the judging system is used for making a difference between the position information of the underwater unmanned aerial vehicle output by the acoustic feature matching and positioning system and the position information of the underwater unmanned aerial vehicle corrected at the last moment, judging the possible jump or frame loss condition of the output data of the underwater unmanned aerial vehicle, and when the difference is within a threshold value range, the positioning result of the acoustic feature matching and positioning system is normal; otherwise, continuing to perform navigation positioning by means of the inertial navigation system, and directly outputting navigation information of the inertial navigation system;
the Kalman filter is used for receiving the heading, attitude and position errors output by the inertial navigation system and taking the heading, attitude and position errors as state estimators; the method is also used for receiving the position information output by the acoustic feature matching positioning system and taking the position information as measurement information so as to estimate the error amount of the position information;
and the inertial navigation system is used for receiving the error amount of the position information fed back by the Kalman filter so as to correct the navigation result of the inertial navigation system.
Due to the adoption of the technical scheme, the invention has the following advantages:
according to the invention, the acoustic characteristic information available in the underwater environment is comprehensively utilized for matching and positioning, the floating water surface on the underwater vehicle or the active emission of the acoustic signal is not needed, the acoustic source information of the existing beacons on the water surface or underwater is only needed to be passively received, and the error correction of the SINS can be realized by adopting a single hydrophone. The system is small in size and suitable for long-distance concealment operation requirements of small underwater unmanned vehicles (Unmanned Underwater vehicle, UUV).
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the embodiments of the present invention, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for positioning assistance in correcting inertial navigation errors based on acoustic feature matching in accordance with an embodiment of the present invention;
FIG. 3 is a conventional match field localization result in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of acoustic feature matching localization results based on neighborhood prior environmental information;
FIG. 5 is a flow chart of acoustic feature matching positioning correction inertial navigation errors in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and examples, wherein it is apparent that the examples described are only some, but not all, of the examples of the present invention. All other embodiments obtained by those skilled in the art are intended to fall within the scope of the embodiments of the present invention.
According to the invention, prior information and channel characteristic parameters of sound source radiation signal propagation of the current environment are obtained, a water sound propagation model suitable for the marine environment is selected, no signal is transmitted, only a broadcast signal periodically transmitted by a surrounding available beacon is required to be passively received, self-positioning is completed by adopting an acoustic feature matching positioning method based on neighborhood prior environment information, and high-precision UUV position information is obtained by combining position, course and attitude information output by an SINS system and by means of a Kalman filtering technology, so that correction of SINS accumulated errors is realized. The method is suitable for the requirements of the small UUV on the concealment when the small UUV needs to acquire the accurate navigation position of the small UUV in the public sea area or the sensitive sea area, the system is simple to realize and small in size, the requirements of remote navigation and autonomous navigation of the UUV are met, the method can be suitable for the accurate navigation positioning of the small UUV under the concealment requirement, and technical support is provided for high-precision navigation in specific application scenes.
The invention provides a system for auxiliary correction of inertial navigation errors based on acoustic feature matching and positioning, which comprises:
the acoustic feature matching and positioning system is used for outputting the position information of the underwater unmanned aircraft;
the judging system is used for making a difference between the position information of the underwater unmanned aerial vehicle output by the acoustic feature matching and positioning system and the position information of the underwater unmanned aerial vehicle corrected at the last moment, judging the possible jump or frame loss condition of the output data of the underwater unmanned aerial vehicle, and when the difference is within a threshold value range, the positioning result of the acoustic feature matching and positioning system is normal; otherwise, continuing to perform navigation positioning by means of the inertial navigation system, and directly outputting navigation information of the inertial navigation system;
the Kalman filter is used for receiving the heading, attitude and position errors output by the inertial navigation system and taking the heading, attitude and position errors as state estimators; the method is also used for receiving the position information output by the acoustic feature matching positioning system and taking the position information as measurement information so as to estimate the error amount of the position information;
and the inertial navigation system is used for receiving the error amount of the position information fed back by the Kalman filter so as to correct the navigation result of the inertial navigation system.
Specifically, the system structure can be designed as shown in fig. 1, and is provided with an external data interface for accessing external sensor information to improve the integrated navigation precision of the system. The main structure is the cylinder structure, roughly divide into five parts, is in proper order: the device comprises an auxiliary navigation correction control module, an inertial navigation module, a depth gauge, an underwater sound signal processing module and a sound head. The sound head part at the bottom of the main body comprises a receiving and transmitting combined transducer and a hydrophone array element array. The underwater acoustic transducer is arranged in the center of the matrix, and can emit acoustic signals, and when the acoustic transducer is used for acoustic feature matching positioning, only the underwater acoustic signals are required to be received, and the acoustic signals are not emitted. The top of the main body is provided with an external interface, comprising a control debugging interface, a data interface, a power interface and the like.
The scheme of the inertial navigation error correction system based on acoustic feature matching positioning in the method is as follows:
aiming at the autonomous hidden accurate navigation positioning requirement of the small unmanned aircraft in the underwater space, the inertial navigation error is corrected by adopting the auxiliary correction based on acoustic feature matching positioning, and under the condition that the UUV does not float on the water surface and actively transmits signals, the accumulated inertial navigation error is corrected, the underwater navigation precision of the UUV is improved, and the method is suitable for the UUV hidden operation requirement. The implementation flow of the inertial navigation error correction based on acoustic feature matching positioning assistance is shown in the attached figure 2, and is specifically described as follows:
the system flow framework comprises an SINS system, an acoustic feature matching and positioning system and a Kalman filter. Wherein the Kalman filter is used for estimating the SINS navigation error, transmitting the navigation error estimation value to the SINS to correct the SINS system error, resetting the navigation parameters, and then taking the corrected navigation parameters as the latest initial condition of the navigation equation.
(1) SINS system
The strapdown inertial navigation system acquires digital signals of a gyroscope and an accelerometer in the strapdown inertial navigation system, inputs the digital signals into a designated navigation computer for resolving, and finally obtains navigation information such as the gesture, the speed, the position and the like of a UUV carrier coordinate system relative to a specific navigation reference coordinate system. In the moving process of the platform, the mounted gyroscope can obtain the movement angular velocity of the UUV relative to the inertial reference system, and based on the movement angular velocity, a transformation matrix of the sound source coordinate system corresponding to the positioning coordinate system can be calculated, so that the acceleration variable obtained through the triaxial accelerometer can be fed back to the positioning coordinate system, and finally, specific navigation information can be obtained through final operation of a computer.
SINS errors are mainly caused by errors of inertial sensors, errors of computer operation processes, errors caused by mathematical models and errors caused by initial alignment. The error caused by the inertial sensor is the main error, and the accumulation of the error with time is increased, so that the error is required to be corrected.
(2) Acoustic feature matching and positioning system
Based on the idea of adopting inverse matching field positioning in acoustic feature matching positioning, the complex structure of the marine environment and a classical acoustic propagation model are fully utilized by combining the characteristics of the marine waveguide, the signal propagation field structure in the marine waveguide is extracted, the relative position of a beacon and a UUV is obtained by utilizing the known absolute position of the beacon sound source through feature matching positioning technology, and finally the absolute position of the UUV is obtained. After the beacon is accurately and absolutely calibrated, the UUV can realize self-positioning only by obtaining the relative coordinate relation between the UUV and the beacon. The positioning principle can be expressed by the following formula:
X UUV ＝X T +X UT
wherein X is UUV Representing absolute coordinates, X, of an underwater vehicle T Representing the absolute coordinate, X of the beacon after precise calibration UT Representing geographyThe coordinates of the vehicle relative to the beacon in the coordinate system.
The difficulty faced by acoustic feature matching positioning is that the positioning performance is greatly reduced due to the mismatch of marine environment parameters. The invention adopts an acoustic feature matching positioning method based on neighborhood priori environmental information aiming at the difficult problem, and the specific flow is as follows:
1) Establishing an environmental acoustic feature information vector library
a) Placing a test sound source in the UUV field to obtain prior information of sound source radiation signal propagation based on the current environment;
b) According to the obtained prior information of the sound source radiation signal propagation of the current environment and channel characteristic parameters (sound velocity profile, environment parameters, underwater sound propagation characteristics and the like), selecting a water sound propagation model suitable for the marine environment, and inputting known marine environment parameters (temperature, salinity, depth and the like) into the model;
c) Along with the position change of the UUV hydrophone, the arrival structure of the received signals is different, namely the relative arrival time delay and amplitude of each intrinsic sound ray are different, and an environmental acoustic characteristic information base is constructed according to the characteristics. Setting a search grid of a hydrophone in a possible occurrence area of a beacon sound source, wherein each grid point is used as a presumed beacon sound source position, known environmental parameters, hydrophone positions and other prior information are adopted, an intrinsic sound ray arrival time delay and amplitude of a test sound source (r, z) reaching a receiving hydrophone are obtained through calculation by utilizing a sound ray theoretical model, and an intrinsic sound ray arrival time delay and amplitude feature library of the beacon sound source reaching the receiving hydrophone is established and used as an environmental acoustic feature information vector library;
d) Obtaining autocorrelation function R of environmental acoustic characteristic signal rplc (r,z)。
2) Acoustic feature information matching and localization
Searching as many eigensound rays as possible and calculating all the additional information of the eigensound rays, including propagation attenuation, propagation delay, etc., and then combining them together. The acoustic feature information matching and positioning process comprises the following steps:
a) Searching as many intrinsic sound rays as possible, calculating all the additional information of the intrinsic sound rays, including propagation attenuation, propagation delay and the like, and then combining the additional information with the propagation attenuation, the propagation delay and the like, and decomposing characteristic parameters such as the arrival angle, the relative arrival delay and the like of the intrinsic sound rays from actual received signals to serve as matching information;
assuming that N eigen sound rays reach the receiving hydrophone, the channel transfer function from the beacon sound source to the receiving hydrophone can be expressed as
Wherein z is s ，z r And r respectively represent the sound source depth, the receiver depth and the distance between the receiver and the sound source; g i Is the amplitude of the ith eigen-line (containing additional phase information) which is a function of signal frequency for a wideband signal; n is n i Is the propagation delay of the ith eigen-acoustic line. The signal transfer function is represented as a superposition of several impulse functions. Each impulse function represents an eigensound ray and represents a propagation path of the signal. The received signal at the receiving hydrophone under this channel model can be expressed as
Where x (n) represents a received signal, s (n) represents a sound source signal, and e (n) represents noise. The arrival time of the intrinsic sound ray can be obtained, so that the estimation of the relative time delay of the intrinsic sound ray can be completed by theoretically utilizing the signal received by the single hydrophone.
b) And carrying out acoustic characteristic information matching operation according to the pre-calculated intrinsic sound ray arrival structure diagram, extracting position characteristic information, and finally obtaining the position relative estimated value of the beacon relative to the UUV.
From the sound ray theory, the position information of the sound source is explicitly contained in the arrival parameters of the intrinsic sound ray. These parameters need to be estimated from the received signal and then the position of the sound source estimated by means of the eigen-acoustic line arrival structure-matching method. An objective function is defined as follows
Wherein w is i As a weighting factor τ data For estimating the relative arrival time delay of the intrinsic sound ray by using measured data, tau rplc And (2) obtaining the relative arrival time delay of the intrinsic sound rays of the test sound source (r, z) reaching the receiving hydrophone by using the sound ray theoretical model by adopting known prior information such as environmental parameters, hydrophone positions and the like in the step 1). In the invention, the beacon is positioned as a known information source signal, and a time delay estimation algorithm with priori information can be adopted.
Assuming that the signal and noise are uncorrelated, the autocorrelation function of the received signal x (n) is
Wherein R is ee As an autocorrelation function of noise e (n), R ss Is an autocorrelation function of the sound source signal s (n). If s (n) is broadband white noise subject to independent same distribution, then its autocorrelation function R ss (m) there is only one peak at m=0.
And (3) directly correlating the autocorrelation function of the environmental acoustic characteristic information with the autocorrelation function of the hydrophone signal by using the environmental acoustic characteristic information vector library constructed in the step 1), so as to estimate the sound source position. The specific algorithm comprises the following steps:
c) And according to the calculated relative positions of the beacon sound source and the UUV and the absolute position information of the beacon in the sound signal, the absolute position of the UUV can be calculated.
Under the condition of parameter mismatch of the complex ocean environment, the simulation distance UUV is 2000m horizontally, and the positioning result of the beacon position at the depth of 10m is shown in the accompanying figures 3 and 4. Therefore, in a complex environment, the acoustic feature matching positioning algorithm based on the neighborhood priori environmental information has excellent positioning performance and good robustness.
(3) Positioning auxiliary correction inertial navigation error based on acoustic feature matching
When inertial navigation is independently used for navigation work, the errors are accumulated continuously along with time, so that the phenomena of drift and divergence of positioning accuracy can be caused. The strapdown inertial navigation system is used as a main reference navigation system, and the acoustic feature matching positioning navigation system is used as an aid, so that the problem of inertial navigation precision divergence can be effectively corrected.
In the integrated navigation system, the participation degree of the sensors is different according to the navigation parameters, and the integrated navigation system is mainly divided into loose combination, tight combination and ultra-tight combination. In the loose combination mode, the acoustic feature matching positioning navigation is selected as an auxiliary navigation system, and the auxiliary navigation system and the SINS constitute a combined navigation system. Classical Kalman filtering is selected as an information fusion means, an indirect estimation method is adopted in the estimation method, and an open loop correction method is used for correcting SINS navigation parameters.
The whole system is coordinated under the action of the control calculation module, and the process is shown in figure 5. The workflow of the system navigation position correction section is as follows:
1) After the UUV is launched, the UUV floats to the near water surface, the rigid antenna is pulled open, the GNSS signal of an initial point is received, and then the UUV enters normal diving;
2) After initialization, UUV can normally dive, the system carries out underwater navigation and positioning processes, SINS outputs the attitude, speed and position information of UUV, and the position error output by SINS increases along with time;
3) The position information of the UUV can be obtained through the resolving of the step (2) based on the acoustic feature matching positioning system (AFMLS for short), the position information is compared with the UUV position information corrected at the last moment, the possible jump or frame loss situation of the output data is judged, when the AFMLS output position information is in the threshold range (the threshold is related to the UUV navigation speed, the sampling interval of the positioning system and the like), the AFMLS positioning result data is normally available, and the step 4 is entered; otherwise, continuing to perform navigation positioning by means of the SINS, avoiding adverse effects on navigation precision when the AFMLS data are abnormal, directly outputting the navigation information of the SINS system, and exerting the characteristic of high short-time precision;
4) The Kalman filter takes heading, attitude and position errors of the SINS as state estimation values, position information provided by the AFMLS as measurement information, error quantity of the position information can be estimated through the filter and then fed back to the SINS to correct the SINS navigation result, and meanwhile, the AFMLS navigation information can be smoothed, so that reliable navigation information corrected by the integrated navigation system is finally obtained.
The system state equation can be expressed as:
wherein X is SINS Is a state variable of SINS, F SINS Is a state matrix of SINS, W SINS/AFMLS Is the state noise of SINS.
The difference between the SINS and AFMLS calculated positions (difference in latitude, difference in longitude, difference in depth) is selected as the observation value. The SINS location information may be expressed as the sum of the true value and the error value:
similarly, AFMLS location information can be expressed as:
the measurement equation for the system can be expressed as:
wherein eta AFMLS Is the measured noise of AFMLS.
The measurement matrix can be expressed as:
H LBL ＝[0 3×6 I 3×3 0 3×6 ]
the acoustic array is mounted on a UUV carrier, and the UUV is positioned in a rectangular earth rectangular coordinate system as (X) UUV ,Y UUV ,Z UUV ) The position in longitude and latitude coordinates is (L UUV ,λ UUV ,Z UUV ). And setting SINS system coordinate reference, and calibrating the acoustic array coordinate system and the carrier coordinate system reference to be consistent.
According to the absolute position information (X) of the earth rectangular coordinate system carried in the beacon sound source information ob ,Y ob ,Z ob ) Or longitude and latitude coordinate information (lambda) ob ,L ob ,Z ob ) And the position (x AFMLS ,y AFMLS ,z AFMLS ) The UUV geodetic rectangular coordinate system absolute position coordinate (X) obtained by acoustic feature matching and positioning calculation can be obtained AFMLS ,Y AFMLS ,Z AFMLS ) Or longitude and latitude coordinates (L AFMLS ,λ AFMLS ,Z AFMLS ) The following formula is shown:
(X AFMLS ,Y AFMLS ,Z AFMLS )＝(X ob ,Y ob ,Z ob )+(x AFMLS ,y AFMLS ,z AFMLS )
SINS output UUV coordinates are expressed as (X SINS ,Y SINS ,Z SINS ) Or (L) SINS ,λ SINS ,Z SINS ) The method comprises the steps of carrying out a first treatment on the surface of the The kalman filter outputs a combined navigation position coordinate representation (X kalman ,Y kalman ,Z kalman ) Or (L) kalman ,λ kalman ,Z kalman )
When SINS needs calibration, UUV inertial navigation system error (δX SINS ,δY SINS ,δZ SINS ) Or (delta L) SINS ,δλ SINS ,δZ SINS ) Error in the geodetic rectangular coordinate system:
(δX SINS ,δY SINS ,δZ SINS )＝(X SINS ,Y SINS ,Z SINS )-(X kalman ,Y kalman ,Z kalman )
errors of inertial navigation system in longitude and latitude geographic coordinate system:
(δL SINS ,δλ SINS ,δZ SINS )＝(L SINS ,λ SINS ,Z SINS )-(L kalman ,λ kalman ,Z kalman )
the error is used for correcting the position information of the inertial navigation system and resetting the navigation parameters.
According to the method and the system for auxiliary correction of inertial navigation errors based on acoustic feature matching positioning, under the condition that the UUV does not float on the water surface and emit signals, the accumulated errors of inertial navigation are corrected, the underwater navigation accuracy of the UUV is improved, and the method and the system are suitable for the requirements of UUV hiding operation;
the system flow framework for correcting inertial navigation errors based on acoustic feature matching positioning assistance provided by the invention, as shown in figure 2, comprises an SINS system, an acoustic feature matching positioning system and a Kalman filter, wherein the Kalman filter is used for estimating SINS navigation errors, transmitting a navigation error estimated value to the SINS to correct the SINS system errors, resetting navigation parameters, and then taking the corrected navigation parameters as the latest initial conditions of a navigation equation;
the acoustic feature matching positioning method is characterized in that the absolute position information of a beacon and a UUV is obtained through a feature matching positioning technology by utilizing the known absolute position information of a beacon sound source, and finally the absolute position information of the UUV is obtained;
according to the acoustic feature matching positioning method based on the marine environment, the complex structure and the classical acoustic propagation model of the marine environment are fully utilized, and the acoustic feature matching positioning method based on the neighborhood priori environmental information is adopted to obtain the priori information based on the propagation of the acoustic source radiation signals of the current environment;
according to the acoustic feature matching positioning-based method, the known environment parameters, priori information of sound source radiation signal propagation, channel feature parameters and other priori information are adopted, a water sound propagation model suitable for the marine environment is selected, the known marine environment parameters are input into the model, and an intrinsic sound ray arrival time delay and amplitude feature library of a beacon sound source reaching a receiving hydrophone are established and used as an environment acoustic feature information vector library;
according to the acoustic feature matching positioning auxiliary correction inertial navigation error, the acoustic feature matching positioning system obtains UUV position information through calculation, compares the position information with UUV position information corrected by the navigation system at the previous moment, judges possible jump or frame loss conditions of output data, when the AFMLS output position information is within a threshold range, positioning result data are normally available, heading, attitude and position error of the SINS are used as state estimation amounts, position information provided by the AFMLS is used as measurement information, SINS error amount is estimated through a filter and fed back to the SINS, correction is carried out on SINS navigation results, meanwhile, smoothing is carried out on the AFMLS navigation information, and finally reliable navigation information corrected by the combined navigation system is obtained.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The method for correcting the inertial navigation error based on acoustic feature matching positioning is characterized by comprising the following steps:
step 1: according to the underwater sound propagation model of the ocean environment and the environmental acoustic feature information base, an environmental acoustic feature information vector base is established;
step 2: based on the environmental acoustic feature information vector library, acoustic feature information matching and positioning are carried out, and position information of the underwater unmanned aircraft is obtained;
step 3: and selecting an acoustic feature matching positioning system as an auxiliary navigation system, forming a combined navigation system with an inertial navigation system, selecting classical Kalman filtering for information fusion, adopting an indirect estimation method, and correcting navigation parameters of the inertial navigation system by using an open loop correction method based on the obtained position information of the underwater unmanned aircraft.
2. The method according to claim 1, wherein the step 1 comprises:
placing a test sound source near the underwater unmanned aircraft to obtain prior information of sound source radiation signal propagation based on the current environment;
according to the obtained prior information of the sound source radiation signal propagation of the current environment and the channel characteristic parameters, selecting a water sound propagation model suitable for the marine environment, and inputting known marine environment parameters into the water sound propagation model; wherein the channel characteristic parameters comprise sound velocity profile, environment parameters and underwater sound propagation characteristics; the marine environmental parameters comprise temperature, salinity and depth;
according to the position change of the hydrophone of the underwater unmanned aircraft, the arrival structure of the received signals is different, namely, the relative arrival time delay and the amplitude of each intrinsic sound ray are different, and an environmental acoustic characteristic information base is constructed;
setting a search grid of a hydrophone in a possible occurrence area of a beacon sound source, wherein each grid point is used as an assumed beacon sound source position, adopting priori information, obtaining the arrival time delay and amplitude of an intrinsic sound ray of a test sound source (r, z) reaching the receiving hydrophone through calculation by utilizing a sound ray theoretical model, and establishing an eigenvoice arrival time delay and amplitude characteristic library of the beacon sound source reaching the receiving hydrophone as an environment acoustic characteristic information vector library; the a priori information includes environmental parameters and hydrophone position.
3. The method according to claim 1, wherein the step 2 comprises:
step 21: searching as many intrinsic sound rays as possible, calculating all the additional information of the intrinsic sound rays, then combining all the additional information of the intrinsic sound rays together, and decomposing characteristic parameters of the intrinsic sound rays from actual received signals to serve as matching information; wherein the intrinsic sound ray additional information comprises propagation attenuation and propagation delay; the characteristic parameters comprise an arrival angle and a relative arrival time delay;
step 22: performing acoustic characteristic information matching operation according to the known intrinsic sound ray arrival structure diagram, extracting position characteristic information, and finally obtaining a position relative estimated value of the beacon relative to the underwater unmanned vehicle;
step 23: and according to the relative estimated value of the position of the beacon relative to the underwater unmanned vehicle and the position information of the beacon in the acoustic signal, the position information of the underwater unmanned vehicle can be calculated.
4. A method according to claim 3, wherein said step 21 comprises:
assuming that N eigen sound rays reach the receiving hydrophone, the channel transfer function from the beacon sound source to the receiving hydrophone is expressed as:
wherein z is s 、z r And r respectively represent the sound source depth, the receiver depth and the distance between the receiver and the sound source; g i Is the amplitude of the ith eigen-acoustic line, n i Is the propagation delay of the ith intrinsic sound ray;
the received signal at the receiving hydrophone is represented as
Where x (n) represents a received signal, s (n) represents a sound source signal, and e (n) represents noise;
and obtaining the arrival time of the intrinsic sound ray based on the received signal at the receiving hydrophone, namely finishing the estimation of the relative time delay of the intrinsic sound ray.
5. A method according to claim 3, wherein said step 22 comprises:
and directly correlating the autocorrelation function of the environmental acoustic characteristic information with the autocorrelation function of the hydrophone signal by using the environmental acoustic characteristic information vector library, so that the sound source position can be estimated.
6. The method according to claim 5, wherein the step 22 specifically includes:
band-pass filtering the received signal of hydrophone and calculating its autocorrelation function R xx ；
The prior information is adopted, and the acoustic line theoretical model is utilized to obtain the arrival time delay and the amplitude of the intrinsic acoustic line of the test sound source (r, z) reaching the receiving hydrophone through calculation; the prior information includes environmental parameters and hydrophone positions;
autocorrelation function R of environmental acoustic characteristic signal rplc Autocorrelation function R of (R, z) and hydrophone signals xx Performing correlation processing to obtain a correlation coefficient rho (r, z);
searching is performed in the expected beacon sound source position area, and a fuzzy plane of the correlation coefficient rho (r, z) is constructed, wherein the peak position of the fuzzy plane is estimated as the relative position of the beacon sound source.
7. The method of claim 6, wherein the autocorrelation function of the received signal x (n) is:
wherein R is ee As an autocorrelation function of noise e (n), R ss Is an autocorrelation function of the sound source signal s (n).
8. The method according to claim 1, wherein the step 3 comprises:
step 31: the position information of the underwater unmanned aircraft can be obtained through the calculation in the step 2, the position information is compared with the position information of the underwater unmanned aircraft corrected at the last moment, the possible jump or frame loss condition of the output data is judged, when the output position information of the acoustic feature matching positioning system is in the threshold range, the positioning result data of the acoustic feature matching positioning system is normally available, and the step 32 is entered; otherwise, continuing to carry out navigation positioning by means of the inertial navigation system, and directly outputting navigation information of the inertial navigation system;
step 32: the Kalman filter takes the course, the gesture and the position error of the inertial navigation system as state estimation values, the position information provided by the acoustic feature matching positioning system is taken as measurement information, the error amount of the acoustic feature matching positioning system is estimated through the Kalman filter and then fed back to the inertial navigation system to correct the navigation result of the inertial navigation system, meanwhile, the navigation information of the acoustic feature matching positioning system can be smoothed, and finally the corrected reliable navigation information of the combined navigation system is obtained.
9. The method according to claim 8, wherein in said step 32:
the system state equation is expressed as:
wherein X is SINS F is a state variable of the inertial navigation system SINS Is a state matrix of an inertial navigation system, W SINS/AFMLS State noise for inertial navigation system;
and selecting the difference between the position information calculated by the inertial navigation system and the acoustic feature matching and positioning system as an observed value, and expressing the position information of the inertial navigation system as the sum of a true value and an error value:
similarly, acoustic feature matching localization system location information can be expressed as:
the measurement equation for the system is expressed as:
wherein eta AFMLS Is the measurement noise of the acoustic feature matching positioning system;
the measurement matrix is expressed as:
H LBL ＝[0 3×6 I 3×3 0 3×6 ]
the acoustic array is arranged on a carrier of the unmanned underwater vehicle, and the position of the unmanned underwater vehicle in a rectangular geodetic rectangular coordinate system is (X) UUV ，Y UUV ，Z UUV ) The position in longitude and latitude coordinates is (L UUV ，λ UUV ，Z UUV ) The method comprises the steps of carrying out a first treatment on the surface of the Setting an inertial navigation system coordinate reference, and calibrating the acoustic array coordinate system and the carrier coordinate system reference to be consistent;
according to the position information (X) of the earth rectangular coordinate system carried in the beacon sound source information ob ，Y ob ，Z ob ) Or longitude and latitude coordinate information (lambda) ob ，L ob ，Z ob ) And the position (x) of the beacon sound source relative to the underwater unmanned vehicle, which is obtained by the acoustic feature matching positioning system AFMLS ，y AFMLS ，z AFMLS ) The position coordinate (X) of the ground rectangular coordinate system of the underwater unmanned aerial vehicle, which is obtained through acoustic feature matching, positioning and resolving, can be obtained AFMLS ，Y AFMLS ，Z AFMLS ) Or longitude and latitude coordinates (L AFMLS ，λ AFMLS ，Z AFMLS ) The following formula is shown:
(X AFMLS ，Y AFMLS ，Z AFMLS )＝(X ob ，Y ob ，Z ob )+(x AFMLS ，y AFMLS ，z AFMLS )
the inertial navigation system outputs an underwater unmanned vehicle coordinate representation (X) SINS ，Y SINS ，Z SINS ) Or (L) SINS ，λ SINS ，Z SINS ) The method comprises the steps of carrying out a first treatment on the surface of the The kalman filter outputs a combined navigation position coordinate representation (X kalman ，Y kalman ，Z kalman ) Or (L) kalman ，λ kalman ，Z kalman )；
When the inertial navigation system needs calibration, the inertial navigation system error (δx of the underwater unmanned vehicle SINS ，δY SINS ，δZ SINS ) Or (delta L) SINS ，δλ SINS ，δZ SINS ) Error in the geodetic rectangular coordinate system:
(δX SINS ，δY SINS ，δZ SINS )
＝(X SINS ，Y SINS ，Z SINS )-(X kalman ，Yk alman ，Z kalman )
errors of inertial navigation system in longitude and latitude geographic coordinate system:
(δL SINS ，δλ SINS ，δZ SINS )
＝(L SINS ，λ SINS ，Z SINS )-(L kalman ，λ kalman ，Z kalman )
the error is used for correcting the position information of the inertial navigation system and resetting the navigation parameters.
10. A system for correcting inertial navigation errors based on acoustic feature matching localization, comprising:
the acoustic feature matching and positioning system is used for outputting the position information of the underwater unmanned aircraft;
the judging system is used for making a difference between the position information of the underwater unmanned aerial vehicle output by the acoustic feature matching and positioning system and the position information of the underwater unmanned aerial vehicle corrected at the last moment, judging the possible jump or frame loss condition of the output data of the underwater unmanned aerial vehicle, and when the difference is within a threshold value range, the positioning result of the acoustic feature matching and positioning system is normal; otherwise, continuing to perform navigation positioning by means of the inertial navigation system, and directly outputting navigation information of the inertial navigation system;
the Kalman filter is used for receiving the heading, attitude and position errors output by the inertial navigation system and taking the heading, attitude and position errors as state estimators; the method is also used for receiving the position information output by the acoustic feature matching positioning system and taking the position information as measurement information so as to estimate the error amount of the position information;
and the inertial navigation system is used for receiving the error amount of the position information fed back by the Kalman filter so as to correct the navigation result of the inertial navigation system.
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