CN104573190B - A kind of method for tracking target based on interactive multi-model - Google Patents
A kind of method for tracking target based on interactive multi-model Download PDFInfo
- Publication number
- CN104573190B CN104573190B CN201410778057.4A CN201410778057A CN104573190B CN 104573190 B CN104573190 B CN 104573190B CN 201410778057 A CN201410778057 A CN 201410778057A CN 104573190 B CN104573190 B CN 104573190B
- Authority
- CN
- China
- Prior art keywords
- target
- turning
- model
- singer
- tracking
- 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.)
- Active
Links
- 230000002452 interceptive effect Effects 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000033001 locomotion Effects 0.000 claims abstract description 41
- 238000001914 filtration Methods 0.000 claims abstract description 22
- 230000001133 acceleration Effects 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 5
- 230000002787 reinforcement Effects 0.000 claims description 2
- 238000004088 simulation Methods 0.000 description 11
- 230000000694 effects Effects 0.000 description 10
- 101100396933 Pseudomonas aeruginosa (strain ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1) imm2 gene Proteins 0.000 description 9
- 101100396930 Pseudomonas aeruginosa imm1 gene Proteins 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 5
- 238000005259 measurement Methods 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Toys (AREA)
- Aiming, Guidance, Guns With A Light Source, Armor, Camouflage, And Targets (AREA)
Abstract
The invention discloses a kind of method for tracking target based on interactive multi-model, including five steps：Step one, according to target dynamic condition, five groups of Singer model parameters are set, five Singer models are built；Five Singer models are interacted formula multi-model nonlinear filtering by step 2, estimate movement velocity, acceleration and the positional information of target；Step 3, using the movement velocity and acceleration of target, calculates target turning angular speed；Step 4, target turning angular speed and the threshold value of setting are compared, judges whether occur turning motion, if turning motion does not occur, regard the positional information obtained in step 2 as target following result；Step 5, in the event of turning motion, the turning angular speed for choosing three adjacent moments builds three Turn Models, interacts formula multi-model nonlinear filtering and obtain target location as target following result.The present invention has the advantages that easily realization, can improve target tracking accuracy, and guarantee is provided for the reliability and accuracy of target following.
Description
Technical Field
The invention relates to a target tracking method based on an interactive multi-model, belonging to the field of target tracking.
Background
In the field of target tracking, an interactive multi-model (IMM) algorithm becomes a widely used target tracking algorithm with excellent performance, and a plurality of filters are used for estimating, interacting and synthesizing a model covering target behaviors to obtain a tracking result conforming to the current target dynamic. In the interactive multi-model algorithm, the accuracy of the model has a great influence on the tracking result.
Turning motion is a common maneuvering form of target tracking, and scholars at home and abroad test maneuvering target tracking algorithms by using turning models. The turning model is mainly modeled by turning angular velocity, and the modeling accuracy of the turning angular velocity of target motion directly influences the modeling accuracy of the turning model. Therefore, to effectively track a target turning maneuver using a turning model, it is necessary to be able to estimate the turning angular rate of the target motion in real time.
A Singer model in the existing target tracking method is a first-order Markov process model, and is modeled according to the intensity of target maneuvering, so that the method is suitable for tracking various maneuvering conditions. However, the Singer model cannot realize high-precision tracking due to its versatility. To achieve higher accuracy tracking, motion modeling is required to be closer to the true motion state.
Therefore, when the target is subjected to turning maneuver, the interactive multi-model nonlinear filtering estimation method firstly carries out interactive multi-model nonlinear filtering estimation on the turning angle speed based on five groups of Singer models, then uses the turning angle speed to model the three-dimensional turning model so as to track the turning maneuver of the target, solves the problem of practicability of using the turning model to track the maneuvering target, and improves the target tracking precision.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a target tracking method based on an interactive multi-model, which is based on an interactive multi-model filtering framework, and when a target makes turning motion, the turning motion model is effectively used for tracking the target by estimating the angular rate of the target motion, so that the tracking precision is improved.
The technical scheme is as follows: a target tracking method based on interactive multi-model, the method is based on interactive multi-model filtering framework, use five Singer models to carry on the interactive filtering and get the position, speed and acceleration information of the target movement first, then calculate the turning angular rate, and compare with threshold value presumed and judge, if the turning angular rate calculated is greater than threshold value presumed, judge the target takes place the turning movement, and carry on the interactive filtering and get the tracking result with three turning models; if the calculated turning angular rate is less than or equal to the set threshold value, the target is considered not to have turning motion, and position estimation obtained by interactive filtering of five Singer models is used as a tracking result, and the method specifically comprises the following steps:
step 1) setting five groups of Singer model parameters according to target dynamic conditions, and constructing five groups of Singer models;
step 2) based on an interactive multi-model structure, carrying out interactive nonlinear filtering on five groups of Singer models to obtain an estimation result of the target, wherein the estimation result comprises position, speed and acceleration information of the target;
step 3) calculating the turning angle by using the tracking speed V (k) and the acceleration a (k) at the moment k obtained in the step 2)Thereby calculating the turning angular rate
Step 4) calculating the turning angular rate and a set threshold value omega0For comparison, if Ω (k)>Ω0If so, determining that the turning motion occurs, and turning to the step 5); if omega (k) is less than or equal to omega0Considering that no turning motion occurs, and taking the target position information estimated in the step 2) as a tracking result;
and 5) if the turning motion is judged to occur, calculating the angular rates of three continuous moments (k, k-1, k-2) according to the step 3), constructing three-dimensional turning models, and estimating by adopting an interactive multi-model nonlinear filtering algorithm to obtain a tracking result.
The Singer model has the specific form:
X(k+1)＝diag[FS(αx),FS(αy),FS(αz)]X(k)+Wk(1)
wherein,i＝x,y,z，αitime constant τ of maneuvering in i-directioniReciprocal of (2), αi＝1/τiK is the filtering time, T is the sampling time, WkFor the system noise vector, the system state variable is X (k) ═ Sx(k),Vx(k),Ax(k),Sy(k),Vy(k),Ay(k),Sz(k),Vz(k),Az(k)]T，Si(k) Is a position in the i direction, Vi(k) Velocity in the i direction, Ai(k) The acceleration rate in the i direction.
Five sets of Singer model parameters (α) in the step 1)x，αy，αz) The specific setting method comprises the following steps:
11) α of the first groupx，αy，αzHas a value interval of [1/50,1/100 ]]Corresponding to the case of a carrier with little or no maneuvering in the x, y, z directions;
12) α of the second groupx，αyHas a value interval of [1/50,1/100 ]]，αzHas a value interval of [1/5,1]Corresponding to the carrier moving less or even no movement in the x, y direction; the case of severe maneuvers in the z direction;
13) α of third groupx，αyHas a value interval of [1/5,1]，αzHas a value interval of [1/50,1/100 ]]Corresponding to the carrier being vigorously maneuvered in the x, y direction; a minor or even no maneuver in the z direction;
14) α of fourth groupx，αy，αzHas a value interval of [1/5,1]Corresponding to the case of a carrier that is severely motorized in the x, y, z directions;
15) the fifth group is a reinforcement group, αx，αyHas a value interval of [1/20,1/5 ]]，αzHas a value interval of [1/30,1/50 ]]The carrier is relatively large in the x, y direction and slow in the z direction.
The specific form of the three-dimensional turning model is as follows:
X(k+1)＝diag[FCT(Ω),FCT(Ω),FCT(Ω)]X(k)+Wk(2)
wherein,omega is the turning angular rate of the target, k is the filtering time, T is the sampling time, WkIs the system noise vector, the state vector is X (k) ═ Sx(k),Vx(k),Ax(k),Sy(k),Vy(k),Ay(k),Sz(k),Vz(k),Az(k)]T，Si(k) Is a position in the i direction, Vi(k) Velocity in the i direction，Ai(k) The acceleration rate in the i direction is, i ═ x, y, z.
The method adopts the turning angle rate estimated in the step 3) as a model parameter to model the three-dimensional turning model in the step 5), effectively uses the turning model to track the turning maneuver of the target, and improves the tracking precision.
By adopting the technical scheme, the invention has the following beneficial effects:
(1) the invention analyzes the maneuvering condition of the three-dimensional maneuvering target, designs five sets of Singer model parameter ranges, and achieves quick and accurate tracking through an interactive multi-model filtering algorithm.
(2) According to the method, when the target is in turning motion, the turning angle rate of the target motion is estimated and solved, the turning model is built, and interactive multi-model target tracking is carried out, so that the target tracking precision is improved, and the practicability of tracking the target by using the turning model is enhanced.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a diagram of a mathematical simulation target motion trajectory according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a comparison of tracking errors of a target in mathematical simulation according to an embodiment of the present invention;
FIG. 4 is a graph illustrating a comparison of mathematical simulation target turning angles and rates according to an embodiment of the present invention;
FIG. 5 is a diagram of a semi-physical simulation target motion trajectory according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a comparison effect of tracking errors of a first section of a semi-physical simulation target according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a comparison effect of tracking errors of a second stage of the semi-physical simulation target according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating a comparison effect of tracking errors of a third section of the semi-physical simulation target according to the embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, in the target tracking method based on the interactive multi-model of the present invention, five sets of Singer model parameters are designed, five Singer models are constructed, and the position, speed and acceleration information of the target motion is obtained by performing interactive estimation under the interactive multi-model filtering framework; then calculating the turning angle rate of the target, comparing the turning angle rate with a set threshold value for judgment, if the turning angle rate obtained by calculation is larger than the set threshold value, judging that the target has turning motion, and carrying out interactive multi-model filtering by using three turning models to obtain a tracking result; and if the calculated turning angular rate is less than or equal to the set threshold value, the target is considered to have no turning motion, and position estimation obtained by interactive filtering of five Singer models is used as a tracking result.
Table 1 is an example of five selected sets of Singer model parameters.
TABLE 1Singer model parameters
| Group of | αx | αy | αz |
| 1 | 1/60 | 1/60 | 1/60 |
| 2 | 1/60 | 1/60 | 1 |
| 3 | 1 | 1 | 1/60 |
| 4 | 1 | 1 | 1 |
| 5 | 1/10 | 1/10 | 1/40 |
In order to verify the feasibility of the invention, mathematical simulation and a semi-physical experiment based on measured data are carried out in a Matlab environment, the sampling time T is 1s, and the turning angular rate threshold value omega is used0And (5) 0.01rad/s, and Gaussian orthogonal integration point Kalman filtering is selected as the nonlinear filter of the interactive multi-model. The position in three directions of x, y and z is selected as a measurement quantity, and the measurement equation is Z (k) ═ HX (k) + v (k), wherein v (k) is a measurement noise vector,for the measurement matrix, the standard deviation σ of the measurement noise is 10 m.
The initial state vector of the filter is X (0) — 20,10,0,10,0,0.3,10,0,0.4]T；
Observing that the variance matrix of noise is R ═ diag [ sigma ]2σ2σ2]；
Initial error variance matrix P0＝diag[p p p]
System noise variance matrix Q of Singer modelSinger＝diag[qSingerqSingerqSinger]
System noise variance matrix Q of turning modelCT(Ω)＝diag[qCT(Ω) qCT(Ω) qCT(Ω)]
The mathematical simulation motion parameters are set as follows, the simulation time is 180s totally, and the motion of the target under the three-dimensional condition is divided into three sections: (1) turn at a turn angle rate of 0.05rad/s for 60 s; (2) performing uniform linear motion for 60 s; (3) turn at a turning angle rate of 0.14rad/s for 60 s; the initial position is [ S ]x(0),Sy(0),Sz(0)]＝[20,10,10]m; initial velocity of [ V ]x(0),Vy(0),Vz(0)]＝[10,10,0]m/s; the motion trajectory is shown in fig. 2.
The position error curves of the tracking results in the x, y and z directions are shown in fig. 3, and the solid line of the IMM1 represents the tracking result of an interactive multi-model composed of a uniform motion model (CV), a uniform acceleration model (CA) and a turning model (CT); the dotted line of IMM2 represents the tracking results of the interactive multi-model composed of the five sets of Singer models of step 2; the dashed line of IMM3 represents the tracking results of the interactive multi-model composed of the three sets of turn models of step 5. As can be seen from FIG. 5, the tracking effect of IMM3 is superior to that of IMM2 and IMM1, and the tracking effect of IMM2 is superior to that of IMM1, during the front 60s and rear 60s turning motions. And the sharper the turn, the more prominent the superiority of the IMM 3. Fig. 4 further illustrates the effectiveness of the method of the present invention, where the set of points in fig. 4 are true values of turning angle rate, the solid line is the turning angle rate calculated by the IMM1 algorithm, the dotted line is the turning angle rate calculated by the IMM2 algorithm, and the dashed line is the turning angle rate calculated by the IMM3 algorithm, it can be seen that the angular rate calculated by IMM3 is closest to the true turning angle rate at the first 60s and the last 60s of the turning motion, and the turning angle rate calculated by IMM2 is the second.
The motion trajectory of the semi-physical experiment is shown in fig. 5. In the semi-physical experiment, the target moves from the point A to the point B, and the movement condition is complex and comprises turning and circling descending movements. In order to clearly compare the tracking effects of the IMM1, IMM2 and IMM3 algorithms, error curves of the tracking results are shown in time segments in FIG. 6, FIG. 7 and FIG. 8. In the time interval of 470-800 s, the target makes a horizontal turning maneuver, and in the tracking result that the maneuvers in the x direction and the y direction are large, the IMM3 has obvious advantages relative to the IMM1, and the IMM3 reduces the dispersion compared with the IMM 2; in the time interval of 1100 s-1600 s, the target makes a turning and descending maneuver of more than 360 degrees, the errors of the IMM3 in the x direction, the y direction and the z direction are all the smallest, and the effect in the y direction is most obvious; in the 1700 s-1850 s interval, the target descends linearly after turning at an obtuse angle, wherein in the 1700 s-1780 s interval, the target makes an obtuse angle turn, the maneuvering in the x direction and the y direction is large, the maneuvering in the y direction and the z direction is large in the 1780 s-1850 s linear descending stage, and in the interval, the tracking effect of the IMM3 is superior to that of the IMM2 and the IMM 1. It can be seen that fig. 6-8 verify that the tracking effect of the IMM3 algorithm is better than that of the IMM2 and the IMM1 and that the tracking effect of the IMM2 is better than that of the IMM1 when the target maneuvers under the semi-physical simulation condition.
In conclusion, the interactive multi-model target tracking method provided by the invention can effectively use the turning model when a turning maneuver occurs, and improve the tracking precision.
Claims (3)
1. A target tracking method based on interactive multi-model is characterized by comprising the following steps:
step 1) setting five groups of Singer model parameters according to target dynamic conditions, and constructing five groups of Singer models; the specific setting method of the five groups of Singer model parameters is as follows:
11) α of the first groupx，αy，αzHas a value interval of [1/50,1/100 ]]Corresponding to the case of a carrier with little or no maneuvering in the x, y, z directions;
12) α of the second groupx，αyHas a value interval of [1/50,1/100 ]]，αzHas a value interval of [1/5,1]Corresponding to the carrier moving less or even no movement in the x, y direction; the case of severe maneuvers in the z direction;
13) α of third groupx，αyHas a value interval of [1/5,1]，αzHas a value interval of [1/50,1/100 ]]Corresponding to the carrier being vigorously maneuvered in the x, y direction; a minor or even no maneuver in the z direction;
14) α of fourth groupx，αy，αzHas a value interval of [1/5,1]Corresponding to the case of a carrier that is severely motorized in the x, y, z directions;
15) the fifth group is a reinforcement group, αx，αyHas a value interval of [1/20,1/5 ]]，αzHas a value interval of [1/30,1/50 ]]The carrier moves more in the x and y directions and slowly moves in the z direction;
step 2) based on an interactive multi-model structure, carrying out interactive nonlinear filtering on five groups of Singer models to obtain an estimation result of the target, wherein the estimation result comprises position, speed and acceleration information of the target;
step 3) calculating the turning angle at the time k by using the tracking speed V (k) and the acceleration a (k) at the time k obtained in the step 2)Further calculating the turning angular rate at the time k
Step 4) calculating the turning angular rate and a set threshold value omega0For comparison, if Ω (k)>Ω0If so, determining that the turning motion occurs, and turning to the step 5); if omega (k) is less than or equal to omega0Considering that no turning motion occurs, and taking the target position information estimated in the step 2) as a tracking result;
and 5) if the turning motion is judged to occur, calculating the angular rates of k, k-1 and k-2 at three continuous moments according to the step 3), constructing three-dimensional turning models, and performing target estimation by adopting an interactive multi-model nonlinear filtering algorithm to obtain a target tracking result.
2. The interactive multi-model-based target tracking method according to claim 1, wherein the Singer model is in the specific form of:
X(k+1)＝diag[FS(αx),FS(αy),FS(αz)]X(k)+Wk(1)
wherein,i＝x,y,z，αitime constant τ of maneuvering in i-directioniReciprocal of (2), αi＝1/τiK is the filtering time, T is the sampling time, WkFor the system noise vector, the system state variable is X (k) ═ Sx(k),Vx(k),Ax(k),Sy(k),Vy(k),Ay(k),Sz(k),Vz(k),Az(k)]T，Si(k) Is a position in the i direction, Vi(k) Velocity in the i direction, Ai(k) The acceleration rate in the i direction.
3. The interactive multi-model-based target tracking method according to claim 1, wherein the three-dimensional turning model is in the following specific form:
X(k+1)＝diag[FCT(Ω),FCT(Ω),FCT(Ω)]X(k)+Wk(2)
wherein,omega is the turning angular rate of the target, k is the filtering time, T is the sampling time, WkIs the system noise vector, the state vector is X (k) ═ Sx(k),Vx(k),Ax(k),Sy(k),Vy(k),Ay(k),Sz(k),Vz(k),Az(k)]T，Si(k) In the i directionPosition, Vi(k) Velocity in the i direction, Ai(k) The acceleration rate in the i direction is, i ═ x, y, z.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410778057.4A CN104573190B (en) | 2014-12-15 | 2014-12-15 | A kind of method for tracking target based on interactive multi-model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410778057.4A CN104573190B (en) | 2014-12-15 | 2014-12-15 | A kind of method for tracking target based on interactive multi-model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104573190A CN104573190A (en) | 2015-04-29 |
| CN104573190B true CN104573190B (en) | 2017-11-03 |
Family
ID=53089244
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410778057.4A Active CN104573190B (en) | 2014-12-15 | 2014-12-15 | A kind of method for tracking target based on interactive multi-model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104573190B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105005686B (en) * | 2015-07-02 | 2017-10-24 | 北京智能综电信息技术有限责任公司 | A kind of method for tracking target of probabilistic forecasting type |
| CN105184027B (en) * | 2015-10-29 | 2018-04-06 | 山东大学 | A kind of power load modelling approach based on interacting multiple model algorithm |
| CN106168943A (en) * | 2016-07-12 | 2016-11-30 | 深圳大学 | A kind of method for following the tracks of turning machine moving-target and system thereof |
| CN106210484A (en) * | 2016-08-31 | 2016-12-07 | 上海鹰觉科技有限公司 | Waters surveillance polynary associating sensing device and cognitive method thereof |
| CN107289968B (en) * | 2017-06-23 | 2018-12-21 | 深圳大学 | A kind of method for estimating state and device of maneuvering target of turning |
| WO2018232740A1 (en) * | 2017-06-23 | 2018-12-27 | 深圳大学 | State estimation method and device for target performing turn maneuver |
| CN107390631B (en) * | 2017-07-14 | 2019-08-09 | 深圳大学 | A kind of track initial method and system for maneuvering target of turning |
| CN109188420B (en) * | 2018-08-27 | 2023-04-07 | 西安电子科技大学 | Narrow-band radar target tracking method based on deep long-short term memory network |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007117586A2 (en) * | 2006-04-08 | 2007-10-18 | Allan Millman | Method and system for interactive simulation of materials |
| CN101465071A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | Multi-platform target tracking and distribution interactive simulation system |
| CN103699713A (en) * | 2013-11-29 | 2014-04-02 | 中国航空无线电电子研究所 | Collision detection method for airplane formation and application of method |
| CN103714199A (en) * | 2013-12-11 | 2014-04-09 | 中国科学院长春光学精密机械与物理研究所 | Target motion characteristic image simulating and outputting system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7472359B2 (en) * | 2004-12-03 | 2008-12-30 | University Of Massachusetts | Behavioral transformations for hardware synthesis and code optimization based on Taylor Expansion Diagrams |
-
2014
- 2014-12-15 CN CN201410778057.4A patent/CN104573190B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007117586A2 (en) * | 2006-04-08 | 2007-10-18 | Allan Millman | Method and system for interactive simulation of materials |
| CN101465071A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | Multi-platform target tracking and distribution interactive simulation system |
| CN103699713A (en) * | 2013-11-29 | 2014-04-02 | 中国航空无线电电子研究所 | Collision detection method for airplane formation and application of method |
| CN103714199A (en) * | 2013-12-11 | 2014-04-09 | 中国科学院长春光学精密机械与物理研究所 | Target motion characteristic image simulating and outputting system |
Non-Patent Citations (6)
| Title |
|---|
| An Improved Target Tracking Singer Filter Algorithm;Hanguang Zhang et al;《2013 3rd International Conference on Computer Science and Network Technology》;20141201;第1070-1073页 * |
| IMM-Singer模型的机动目标跟踪算法;谭顺成等;《火力与指挥控制》;20120215;第37卷(第2期);第32-34页 * |
| 一种参数自适应调整的Singer模型跟踪算法;邵俊伟;《科技信息》;20140505(第13期);第111-112页 * |
| 基于转弯模型的自适应交互式多模型跟踪算法;肖卫东等;《指挥控制与仿真》;20090415;第31卷(第2期);第36-41页 * |
| 自适应转弯模型的交互多模型算法研究;王华楠等;《弹箭与制导学报》;20081015;第28卷(第5期);第241-244页 * |
| 转弯机动目标的两层交互多模型跟踪算法;孙福明等;《控制理论与应用》;20080415;第25卷(第2期);第233-236页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104573190A (en) | 2015-04-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104573190B (en) | A kind of method for tracking target based on interactive multi-model | |
| CN104713560B (en) | Multi-source distance measuring sensor spatial registration method based on expectation maximization | |
| CN108304612B (en) | Iterative square root CKF (tracking of target) automobile radar target tracking method based on noise compensation | |
| CN109885883A (en) | A kind of control method of the unmanned vehicle transverse movement based on GK clustering algorithm model prediction | |
| CN103644903B (en) | Synchronous superposition method based on the tasteless particle filter of distributed edge | |
| CN108762309B (en) | Human body target following method based on hypothesis Kalman filtering | |
| CN103047982B (en) | Adaptive target tracking method based on angle information | |
| CN111178385A (en) | Target tracking method for robust online multi-sensor fusion | |
| CN110595479B (en) | SLAM track evaluation method based on ICP algorithm | |
| CN113253717B (en) | Indoor mobile robot local path planning method based on dynamic barrier | |
| CN104898104A (en) | Target combined positioning method based on Euler's distance means clustering | |
| CN103176409A (en) | Method for fast and accurately realizing concrete pump truck cantilever crane movement locus | |
| CN103308896A (en) | High-precision tracking method applied to non-engine maneuvering target | |
| CN103973263B (en) | Approximation filter method | |
| CN110345936B (en) | Track data processing method and processing system of motion device | |
| CN108267731A (en) | The construction method of unmanned plane target tracking system and application | |
| CN104777465B (en) | Random extended object shape and state estimation method based on B spline function | |
| CN108303095B (en) | Robust volume target cooperative localization method suitable for non-Gaussian filtering | |
| CN107643083B (en) | Spatial target interruption track correlation method based on track prediction | |
| CN106707967B (en) | Three-D profile error estimation and device based on approximate test expansion | |
| RU2692837C2 (en) | Method for determining parameters of movement of noisy object | |
| CN109164419B (en) | Multi-platform disorder measurement processing method based on interactive multi-model | |
| CN107391446A (en) | Irregular shape based on random matrix extends target shape and method for estimating state more | |
| Cheng et al. | Nonlinear observer of sideslip angle using a particle filter estimation methodology | |
| CN113763434B (en) | Target track prediction method based on Kalman filtering multi-motion model switching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |