CN112946625B - B-spline shape-based multi-extended target track tracking and classifying method - Google Patents

B-spline shape-based multi-extended target track tracking and classifying method Download PDF

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CN112946625B
CN112946625B CN202110166401.4A CN202110166401A CN112946625B CN 112946625 B CN112946625 B CN 112946625B CN 202110166401 A CN202110166401 A CN 202110166401A CN 112946625 B CN112946625 B CN 112946625B
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杨金龙
李方迪
陶九六
刘建军
葛洪伟
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Jiangnan University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates

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Abstract

The invention discloses a B-spline shape-based multi-extended target track tracking and classifying method, which relates to the technical field of information processing and comprises the following steps: firstly, the irregular shape estimated by the B spline is used in a KDE-SSP method, the adjacent target measurement set is divided for the second time, and the center position of the candidate shape is searched by using a kernel density estimation method, so that the efficiency of the algorithm is improved; and then, target shape category information is extracted to assist the updating and extraction of the target state, so that the problems of missing tracking, wrong tracking and the like of updating of an adjacent target measurement set are solved, and meanwhile, the track management and the target classification of the extended target are effectively realized according to the extracted target motion state, the target shape information and the target track.

Description

B-spline-shape-driven multi-extension target track tracking and classifying method
Technical Field
The invention relates to the technical field of information processing, in particular to a B-spline-shape-based multi-extension target track tracking and classifying method.
Background
The research on the multi-target tracking (MTT) technology has been a key point in the target tracking problem, and has also been a difficult point, and has been widely applied to the fields of civilian use, military use, and the like, such as tracking systems for video monitoring and the like, monitoring systems for early warning of enemies, missile-borne systems, sea, land, air and multi-dimensional integrated cooperative systems, and the like.
The MTT mainly utilizes observation information obtained by a sensor to estimate the state of a target, but because many uncertain interference factors exist in a complex real scene, the observation information is inaccurate, and a great challenge is brought to tracking. Early multi-target tracking mostly adopts the idea of data association, such as Multiple Hypothetic Tracking (MHT), Joint data association (JPDA), etc., but these methods have too high time complexity when the number is increased, and it is difficult to effectively process multi-target tracking with a changed number. Aiming at the problem, in Mahler and 2003, a Random Finite Set (RFS) multi-target tracking theory is proposed in a document Multi target Bayes filtering sight-order multi target movements, so that the uncertain and variable multi-target tracking can be effectively processed, and a new thought is provided for the multi-target tracking.
With the increasing resolution of modern radar and other novel detection devices, the echo signals of a target may be distributed in different range resolution units, the detection field of the target is no longer equivalent to one point, the target may produce multiple measurements, and a target with such characteristics is called an Extended target. In the conventional point target tracking problem, the assumption that one target corresponds to one measurement is no longer true, but the problem that a plurality of measurements correspond to the same target is solved. In 2009, Mahler and the like further popularize the RFS theory to the multi-extension target tracking field, provide a multi-extension target measurement updating frame, provide a new idea for multi-extension target tracking, become the leading edge and hot spot problems of the research of the extension target tracking field, and particularly have great challenges for irregular-shape multi-extension target tracking under the uncertain observation complex environment. Random matrix technology is introduced into document A PHD Filter for Tracking Multiple Extended Targets Using Random matrix, and a Gaussian inverse Weishate (GIW-PHD) filtering method is proposed to realize the shape estimation of the Extended target, but only the shape of the target can be estimated to be an ellipse; subsequently, a Random Hypersurface technology is introduced in the document Extended Object and Group Tracking with orthogonal Random Hypersurface Models to further realize star-shaped multi-Extended target Tracking. However, the algorithms do not fully consider the shape information of the extended targets, track tracking and target classification of the extended targets are realized, especially when multiple extended targets are adjacent, the tracking precision is seriously reduced, and aiming at the problem, the invention provides a method for driving the track tracking and the classification of the multiple extended targets based on the B-spline shape.
Disclosure of Invention
Aiming at the problems and the technical requirements, the invention provides a method for tracking and classifying multi-extended target tracks based on B-spline shape drive, and the technical scheme of the invention is as follows:
a B-spline-shape-based multi-extension target track tracking and classifying method comprises the following steps:
establishing a B-Spline-GM-PHD filter, initializing a target state at the moment k:
Figure GDA0003571889510000021
wherein x iskRepresents a state of motion, BkRepresenting B-spline shape information, pk,i、θk,iRespectively represents the polar diameter and polar angle information at the ith angle at the moment k, LkA track label representing the gaussian component at time k,
Figure GDA0003571889510000022
and a track label representing the jth Gaussian component at time k.
When k is larger than or equal to 1, performing primary division on the measurement set by a distance division method;
if the divided measurement set has an adjacent target, performing secondary division on the divided measurement set, and if the divided measurement set does not have an adjacent target, performing multi-hypothesis filtering on the target state by adopting a B-Spline-GM-PHD filter;
performing multi-hypothesis filtering on the target state by adopting a B-Spline-GM-PHD filter to update the target state and obtain a shape likelihood function after weight updating;
fusing target components meeting a distance fusion threshold, pruning Gaussian components with undersized weights after fusion, taking the Gaussian components with high weights as extraction targets in the fusion process, and extracting the targets comprising target motion states, target shape information and target tracks;
the likelihood is obtained by the shape likelihood function for the extracted target shape information and the preset target shape with the category information, and the target shape category information is obtained;
and if the observation information arrives at the next moment, re-executing the step of dividing the measurement set once by using the distance division method, otherwise, ending the target track tracking process.
The beneficial technical effects of the invention are as follows:
the application provides a method for tracking and classifying multi-extension target tracks based on B-spline shape driving, aiming at the phenomenon of tracking missing and wrong tracking when the shape information of an adjacent target is not fully considered in the multi-extension target tracking based on ET-GM-PHD. The method adopts a B spline fitting method to realize the estimation of any irregular shape of the extended target, fully excavates the shape information of the extended target, adopts a kernel density shape partitioning (KDE-SSP) method to carry out secondary partitioning on a measurement set of the adjacent extended target, extracts target shape category information to assist the update and extraction of the target state, effectively improves the tracking performance of the adjacent extended targets, and effectively realizes the track management and the target classification of the extended target according to the extracted target motion state, the target shape and the target track.
Drawings
FIG. 1 is a flow chart of multi-extended target tracking based on B-Spline-GM-PHD provided by the present application.
FIG. 2 is a diagram of a metrology set W in which two extended targets are in close proximity.
FIG. 3 is a three-dimensional nuclear density estimate plot of the metrology in the immediate vicinity of the extended target metrology set W.
FIG. 4 is a schematic diagram of the measure point determination in the KDE-SSP algorithm.
Fig. 5 is a polar angle and radius map after dimension expansion in the shape matching algorithm.
Fig. 6 is a polar angle and diameter map after processing by a shape matching algorithm.
FIG. 7 is a graph of the effect of the KDE-SSP algorithm in partitioning the set of proximate target metrics.
FIG. 8 is a diagram of the effect of the B-Spline-GM-PHD algorithm without shape information update.
FIG. 9 is a diagram illustrating the effect of the updated B-Spline-GM-PHD algorithm provided by the present application.
FIG. 10 is a graph of track following results without shape information update.
FIG. 11 is a graph of the results of track following two targets.
FIG. 12 is a graph illustrating the average OSPA distance and the weights and sums of two targets.
Fig. 13 is a diagram of the result of shape classification of two objects.
FIG. 14 is a graph of two target average shape classification accuracy.
FIG. 15 is a graph showing the classification results and the average classification accuracy for three targets.
FIG. 16 is a graph of the results of track following three targets.
FIG. 17 is a graph illustrating three target average OSPA distances.
FIG. 18 is a graph of three target average weight sums.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
The application discloses a B-spline-shape-based multi-extension target track tracking and classifying method, a flow chart of which is shown in figure 1, and the method comprises the following steps:
step 1: establishing a B-Spline-GM-PHD filter, and initializing a target state at the moment k:
Figure GDA0003571889510000041
wherein x iskRepresents a state of motion, BkRepresenting B-spline shape information, pk,i、θk,iRespectively represents the polar diameter and polar angle information at the ith angle at the moment k, LkA tag representing the gaussian component track at time k,
Figure GDA0003571889510000042
and (3) associating the same labels at different moments by using the track label representing the jth Gaussian component at the moment k, so as to realize association between tracks.
Step 2: and when k is more than or equal to 1, dividing the measurement set once by a distance division method.
And step 3: and (4) judging whether a plurality of measurement neighbors exist in the measurement set after the primary division, if so, entering the step 4, and if not, entering the step 5.
And 4, step 4: if the divided measurement set has an adjacent target, performing secondary division on the divided measurement set, including:
step 41: initializing a candidate shape parameter set C when k is 1:
Figure GDA0003571889510000043
wherein,
Figure GDA0003571889510000044
candidate shape parameters for fitting a B-spline curve,
Figure GDA0003571889510000045
respectively representing polar diameter and polar angle information at the ith angle; initializing polar angles
Figure GDA0003571889510000046
Is composed of
Figure GDA0003571889510000047
n1Is a predetermined dimension, and n1< 360, and the polar diameter information corresponding to the initialized polar angle is
Figure GDA0003571889510000048
Alternatively, suppose n1When 20, the polar angle is initialized to
Figure GDA0003571889510000049
When k is more than or equal to 2, selecting m targets with weight omega more than 0.5 in the posterior target state, and putting the shape information of the m posterior target states into the candidate shape parameter set
Figure GDA00035718895100000410
In the method, the targets with the weight omega more than 0.5 are selected to play a role in removing clutter.
Step 42: determining the central point position of each candidate shape in the candidate shape parameter set C by adopting a Kernel Density Estimation (KDE), and establishing a candidate central position set L:
Figure GDA00035718895100000411
wherein the coordinates corresponding to the density peak points of the candidate shape to which the B-spline curve is fitted are represented by (ω)q (x)，ωq (y)) And n represents the number of density peak points.
Assume that the metrology set W contains two closely adjacent targets, representing metrology sets of multiple targets of the same metrology set. Since the candidate shape in extended target tracking is the result of fitting with a B-spline curve, all measured density peak points within the shape may be approximated as the candidate shape center point.
Specifically, the shape in the whole filtering process is fitted by using a B-spline, and the method for fitting the shape by using a B-spline curve comprises the following steps:
b-spline curve is composed of B-spline basis function Ni,k(u) and control vertex PiCollectively determined, the k-th order B-spline function is represented as:
Figure GDA0003571889510000051
wherein, Ni,k(u) is a k-1 degree B-spline basis function whose recurrence formula is:
Figure GDA0003571889510000052
wherein U is { U ═1,u2,…,uk,…,un+kIs a set of node vectors for the B-spline.
When k is 3, the B-spline basis function is expressed as:
Figure GDA0003571889510000053
the process of obtaining the control vertex set in the B-spline function by using the position information of the measurement points, in which the measurement set of the extended target includes a plurality of measurement points, is as follows:
suppose that
Figure GDA0003571889510000056
In order to expand the measurement set of the target, the mean position of the measurement set is calculated, the mean position is used as the origin of coordinates to establish a coordinate system, the coordinate system with the angle of 2 pi is divided into n equal parts to obtain n control vertex positions, and the polar coordinates are used for representing the control vertexes Lambdai＝{ρi,θi}，ρi、θiRespectively, the pole diameter and the pole angle, and the pole diameter is expressed as:
Figure GDA0003571889510000054
|Zi|＝{zk|d(zk,βi)＜λ1,k(zk,αi)＝1}
Figure GDA0003571889510000055
βi＝[-tan(θi)，1]
wherein, | - | represents the number of elements, d (z)k，βi) Indicates the measurement point zkTo betaiDistance of line, λ1Distance threshold for constraint width, κ (z)k，αi) As a constraint, βiIs polar angle thetaiThe calculation formula of the determined vector, d (-) is as follows:
Figure GDA0003571889510000061
αi＝[1，tan(θi)]
wherein, | | · | | represents an absolute value, αiRepresentation and vector betaiThe vector perpendicular to the straight line.
And through the calculation, solving a control vertex set Lambda under polar coordinates, converting the control vertex set Lambda into a corresponding set P under a rectangular coordinate system, and generating a smooth curve related to the shape of a measurement set under the combined action of the control vertex set Lambda and a B spline basis function.
FIG. 2 shows two metrology sets W of two closely spaced targets, which include all of the measurement points of two extended targets, one of which is a "Y-shaped" target and the other of which is a "cross-shaped" target. By using a Gaussian kernel density function and selecting a proper bandwidth, a three-dimensional density estimation graph of two-dimensional discrete point coordinates in the measurement set W shown in FIG. 3 can be obtained. In the density map, the coordinates corresponding to each density peak point can be easily found
Figure GDA0003571889510000062
Step 43: the candidate shape parameter set C and the candidate center position set L are combined into
Figure GDA0003571889510000063
The method comprises m × n combinations, and the Shape Selection Partitioning (SSP) is adopted to partition and combine the m × n combinations to obtain an in-Shape measurement subset and an out-Shape measurement subset, wherein the in-Shape measurement subset and the out-Shape measurement subset are respectively:
Figure GDA0003571889510000064
Figure GDA0003571889510000065
wherein,
Figure GDA0003571889510000066
a subset of the measurements within the shape is represented,
Figure GDA0003571889510000067
representing a subset of extratopographic measurements, zkRepresents the measurement points in the measurement set W,
Figure GDA0003571889510000068
indicates the measurement point zkTo the central point position omegaqThe distance of (c). As shown in FIG. 4, phi denotes
Figure GDA0003571889510000069
The point of the corresponding point is displayed on the display,
Figure GDA00035718895100000610
representing the angle between the connecting line of the central point position and the h-th point on the shape and the x-axis,
Figure GDA00035718895100000611
an angle between a connecting line representing the position of the center point and the measuring point and the x-axis is set, and when the absolute value of the difference between the two is minimum, a point on the shape at the angle is selected as psi,
Figure GDA00035718895100000612
representing psi to center point position omegaqOf the distance of (c).
Figure GDA00035718895100000613
The calculation formula of (c) is as follows:
Figure GDA00035718895100000614
Figure GDA00035718895100000615
Figure GDA0003571889510000071
wherein, bΨVector, p, representing the location of the center point to Ψk|k-1Coordinate information representing control vertices generated from the intra-shape metrology subset, Ni,3(ψ) represents a B-spline basis function.
Each combination may be in the region of the metrology set W, exactly ωqAs the position of the center point,
Figure GDA0003571889510000072
for the fitted shape of the parameterAnd (4) counting. In addition, in the judgment of the measuring points, due to the influence of clutter or sensor instability, the measuring points closer to the edge are likely to be misjudged as out-of-shape points, and the shape parameters for segmentation are expanded by 1.3 times in the calculation process, wherein the times are empirical times obtained through multiple experiments. In fig. 4, the inner circle curve represents the unexpanded parting line, the outer circle curve represents the parting line expanded by 1.3 times, and when the shape parameter is expanded by 1.3 times, the shape edge measuring point can be well divided into the shape without affecting the division of the adjacent target measuring point.
And step 44: respectively matching the shape internal measurement subset and the shape external measurement subset to obtain the likelihood, and obtaining the condition of the maximum weight product of the shape internal measurement subset and the shape external measurement subset, wherein the condition comprises the following steps:
for intra-shape measurement subsets
Figure GDA0003571889510000073
The likelihood is calculated using the shape used when the intra-shape metrology subset fits the shape and the segmentation combination, and the process of calculating the likelihood is a process of updating the shape likelihood function with a B-spline-GM-PHD filter.
For out-of-shape measurement subsets
Figure GDA0003571889510000074
Solving the weights by using the same updated shape likelihood function process, traversing all candidate shapes to respectively obtain likelihood with a measuring set fitting shape, and selecting the maximum value of the weights as an external measuring subset
Figure GDA0003571889510000075
And finding the condition that the likelihood product of the shape internal measuring subset and the shape external measuring subset is maximum as the final segmentation result.
And 5: and performing multi-hypothesis filtering on the target state by adopting a B-Spline-GM-PHD filter, updating the target state, and acquiring the shape likelihood function after the weight is updated.
Step 51: sequentially carrying out target prediction and target update of a B-Spline-GM-PHD filter on the target state, and specifically comprising the following steps:
the prediction step of the B-Spline-GM-PHD filter is represented as:
Figure GDA0003571889510000076
Figure GDA0003571889510000077
Figure GDA0003571889510000078
Lk|k-1＝Lk-1∪Lβ
wherein,
Figure GDA0003571889510000081
representing the probability hypothesis density of the predicted target, Jk|k-1Represents the number of predicted gaussian components at the moment k,
Figure GDA0003571889510000082
respectively representing the weight, mean and covariance matrix of the jth Gaussian component predicted at the time k, N (-) is Gaussian distribution, Lk|k-1And the label set representing the prediction at the time k comprises the label sets of the original target and the new target at the time k-1, and the predicted polar diameter and polar angle information are consistent with the state of the posterior target at the time k-1.
Mean value
Figure GDA00035718895100000813
Sum covariance matrix
Figure GDA00035718895100000814
Are respectively expressed as:
Figure GDA0003571889510000083
Figure GDA0003571889510000084
wherein, Fk|k-1Represents a state transition matrix, IdRepresenting a d-dimensional identity matrix, QkRepresenting the process noise covariance matrix.
The update steps of the B-Spline-GM-PHD filter are represented as:
Figure GDA0003571889510000085
wherein, the probability hypothesis density of the missed detection target is expressed as:
Figure GDA0003571889510000086
Figure GDA0003571889510000087
Figure GDA0003571889510000088
Figure GDA0003571889510000089
Figure GDA00035718895100000810
Figure GDA00035718895100000811
Lk|k＝Lk|k-1
wherein, γjA desired value representing the number of target production measurements,
Figure GDA00035718895100000812
representing a target detection probability;
the probability hypothesis density for target detection at time k is given by:
Figure GDA0003571889510000091
Figure GDA0003571889510000092
Figure GDA0003571889510000093
Figure GDA0003571889510000094
Figure GDA0003571889510000095
Figure GDA0003571889510000096
Figure GDA0003571889510000097
where σ is expressed as a preset forgetting factor, ρW，iFor the polar diameter information at the ith polar angle of the shape to which the current measurement set is fitted,
Figure GDA0003571889510000098
the sum of the measurement set numbers in all the division methods is represented, and each innovation parameter is represented as:
Figure GDA0003571889510000099
Figure GDA00035718895100000910
Figure GDA00035718895100000911
Figure GDA00035718895100000912
Figure GDA00035718895100000913
step 52: the weight update formula considering the target shape information is:
Figure GDA00035718895100000914
Figure GDA00035718895100000915
Figure GDA00035718895100000916
wherein,
Figure GDA00035718895100000917
the position likelihood of the current measurement set is shown, and it should be noted that in the above formula
Figure GDA0003571889510000101
In shorthand form, the position likelihood is solved using a gaussian distribution,
Figure GDA0003571889510000102
show the shape likeThe calculation process of the shape matching likelihood of the function, namely the B spline, is as follows:
Figure GDA0003571889510000103
Figure GDA0003571889510000104
wherein HW、RWDenotes an innovation parameter, r'k|k-1,iRepresents the post-treatment predicted pole diameter, r'W,iDenotes the measured collector diameter after processing, and λ denotes the shape similarity index.
There are two cases for the shape likelihood function, when r'k|k-1,i|＝|r′W,iIf not, the shape likelihood is calculated by the second formula.
The processing method of the pole diameter comprises the following steps:
known predicted path information
Figure GDA0003571889510000105
Polar diameter information corresponding to the current measurement set
Figure GDA0003571889510000106
Two polar diameter information are divided into n1The dimension expansion is 360 dimensions, resulting in:
Figure GDA0003571889510000107
Figure GDA0003571889510000108
as shown in FIG. 5, r can be obtained in a two-dimensional rectangular coordinate systemk|k-1And rWPolar angle and polar diameter diagram (1). FIG. 6 shows r 'after polar angle and polar diameter processing after dimensional expansion'k|k-1，iAnd r'W，iCorresponding poleAngle pole diameter diagram. From predicted polar diameter information r after dimension expansionk|k-1And measuring collector radius information r after dimension expansionWThe peak values of the wave crest and the wave trough obtained from the polar angle polar diameter diagram are respectively Pk|k-1And PWThe corresponding peak numbers are respectively
Figure GDA00035718895100001010
And
Figure GDA00035718895100001011
by comparing the peak numbers of the two, the predicted polar diameter information after dimension expansion and the measured polar diameter information after dimension expansion are reordered respectively, and then the polar angle information is correspondingly changed, specifically comprising:
(1) when in use
Figure GDA0003571889510000109
The method comprises the following steps:
case 1, match to two peak points:
Pk|k-1(1,1:2)≈PW(1,u0:v)
Figure GDA0003571889510000111
v＝mod(u0+1,Num),or v＝Num
r′k|k-1＝[rk|k-1(lk|k-1(1)),…,rk|k-1(end),rk|k-1(1),…,rk|k-1(lk|k-1(1)-1)]
r′W＝[rW(lW(u0)),…,rW(end),rW(1),…,rW(lW(u0)-1)]
λ＝1
case 2, matching to a peak point:
Pk|k-1(1)≈PW(u0)
Figure GDA0003571889510000112
r′k|k-1＝[rk|k-1(lk|k-1(1)),…,rk|k-1(end),rk|k-1(1),…,rk|k-1(lk|k-1(1)-1)]
r′W＝[rW(lW(u0)),…,rW(end),rW(1),…,rW(lW(u0)-1)]
Figure GDA0003571889510000113
case 3, no peak point matched:
Pk|k-1(1)≠PW(u0)
Figure GDA0003571889510000114
r′k|k-1＝rk|k-1
r′W＝rW
Figure GDA0003571889510000115
(2) when in use
Figure GDA0003571889510000116
The method comprises the following steps:
r′k|k-1＝rk|k-1
r′w＝rw
Figure GDA0003571889510000117
wherein "≈" indicates that the peak size of the predicted shape is equal to the peak size of the metrology set shape within the threshold range.
Therefore, the shape likelihood function of the measurement set is updated, and the updated shape likelihood function formula is used for updating the likelihood of a target state, target shape classification, track tracking and the like, so that the filtering result is more accurate.
Step 6: setting a distance fusion threshold U, fusing target components meeting the distance fusion threshold U, pruning Gaussian components with undersized weights after fusion, taking the Gaussian components with high weights as extraction targets in the fusion process, and extracting the targets comprising target motion states, target shape information and target tracks.
If the number t of the fused Gaussian components is larger than the preset maximum threshold J of the Gaussian componentsmaxTaking J out of the fused Gaussian componentsmaxAnd (4) completing pruning operation by the Gaussian component with the maximum weight value.
And in the fusion process of corresponding Gaussian components of the track label, selecting the track label with the maximum weight value of the Gaussian components as the fused track label.
And 7: the likelihood of the extracted target shape information and a preset target shape with category information is solved through a shape likelihood function, and the target shape category information is obtained, and the method comprises the following steps:
to achieve classification of the target shapes, a set of preset target shapes with class information is set as
Figure GDA0003571889510000121
The set including a predetermined "Y-shape
Figure GDA0003571889510000122
'and' cross shape
Figure GDA0003571889510000123
"class information, i.e
Figure GDA0003571889510000124
Wherein
Figure GDA0003571889510000125
Using all the extracted object shape informationSet Bk|kEach shape of (2) is respectively integrated with a preset target shape
Figure GDA0003571889510000126
The likelihood is obtained for each shape, and the formula is obtained by using the shape likelihood function of the B-Spline-GM-PHD filter
Figure GDA0003571889510000127
Solving for each extracted shape
Figure GDA0003571889510000128
All have the shapes with the maximum likelihood of matching
Figure GDA0003571889510000129
When the likelihood exceeds a category threshold, the extracted s-th shape is classified into the c-th class, and when the likelihood is smaller than the category threshold, the extracted s-th shape is classified into other classes, namely the s-th shape cannot be accurately classified into a preset target shape at the first moments of filtering, so that the s-th shape is classified into other classes, and the s-th shape can be matched with the corresponding preset target shape after the likelihood exceeds the category threshold.
And 8: and if the observation information arrives at the next moment, re-executing the step of dividing the measurement set once by using the distance division method, otherwise, ending the target track tracking process.
In order to verify the effect of the B-Spline-GM-PHD filtering algorithm and the KDE-SSP method adopted by the application, the design experiment is as follows:
1. implementation conditions and parameters
In a two-dimensional plane monitoring area [ -1000,1000] × [ -1000,1000], the number of targets in the monitoring area is unknown and varies with time. Setting two targets to have adjacent cross motion at the same time, and carrying out experimental analysis on the measurement set of filtering at the current time. In addition, the OSPA distance of the target, the weight of the target, and the shape classification accuracy were recorded in 100 experiments.
2. Software, hardware and related parameter setting in experimental process
The method is carried out on a machine with a processor of AMD Ryzen 74700U with Radon Graphics, 4.1GHz and 8 cores, a memory of 16GB and a display card of AMD Radon (TM) Graphics, and is written by Matlab R2020a software.
The target states set in the experiment were:
ζk＝{xk，Bk，Lk}
the parameters in the motion and metrology models are as follows:
Figure GDA0003571889510000131
Figure GDA0003571889510000132
Figure GDA0003571889510000133
Figure GDA0003571889510000134
the experiment makes uniform linear motion under the sampling period of T-1 s, wherein sigma isa＝2m/s2、σg3 m. Probability of detection pD0.99, survival probability psEach time step, the number of generated target measurements is poisson's ratio γ 10.
The new target intensity is expressed as:
Db＝0.1N(ζk，mb，Pb)
3. qualitative analysis of the results
The specific experiment mainly aims at the division of measurement sets at the time of the close proximity of multiple extended targets, the influence of a likelihood updating method on position, flight path and shape classification is evaluated, the influence on the accuracy of an experiment result when a target is newly generated is evaluated, and the experiment result is as follows:
experiment one: proximity extension target measurement set partitioning
In the process of movement, when two targets are close to each other, the distance between the measurement sets of the two extended targets detected by the sensor is short, and the measurement sets are easily wrongly divided into one measurement set by using a traditional measurement set dividing method. Therefore, the KDE-SSP algorithm is adopted for measurement set division of the adjacent targets.
In the uniform linear motion at the time 100, the targets move closely at the three times 47, 48 and 49. As shown in fig. 7, are the results of partitioning the measurement set using the KDE-SSP algorithm for two closely adjacent targets at time 48. It can be seen that the present invention can better divide two closely adjacent target metrology sets.
Experiment two: two target position tracking
As shown in fig. 8, at time 48, in the measurement update of the target close to the ET-GM-PHD, the target update is performed by using the position information of the measurement set that is subject to the gaussian distribution, and the phenomenon of missing and missing the target due to the erroneous update of the measurement set occurs, and at time 48 shown in the figure, only the "cross-shaped" target is tracked, which results in the "Y-shaped" missing, and at time 49, the "Y-shaped" target is missed. Therefore, at the time of the immediate vicinity of the target, the likelihood update is prone to errors using only the position information.
As shown in fig. 9, at time 48 as well, shape information is added to the likelihood of the update process, so that the problems of wrong tracking and missed tracking when the targets are close to each other are solved well.
Meanwhile, the figure also shows a comparison experiment of the B-spline method and the shape estimation method of the GIW-PHD, and the accuracy of the B-spline estimation shape can be seen.
Experiment three: two target track tracking
As shown in fig. 10, the weight is updated only by using the position information in the target tracking, and fig. 10- (1) shows the track tracking result at 100 times, and fig. 10- (2) shows that the track is updated only by using the position information, which may cause the phenomena of tracking error and tracking missing.
In the application, the track tracking is to add a track label in a Gaussian component. And performing track association by using the same label at different time. In the track association of the adjacent targets, the maximum weight is obtained by the likelihood obtaining method of the application, and the track association is carried out. As shown in fig. 11, a graph of the track following results using the filter likelihood update of the present invention. Fig. 11- (1) is a track tracking result at 100 time instants, and fig. 11- (2) is a detailed diagram of the time instants immediately adjacent to each other, and it can be seen from fig. 11- (2) that correct track tracking can still be performed at the time instants immediately adjacent to each other by using the filtering algorithm of the present invention.
To evaluate the quasi-certainty and stability of the invention, 100 experiments under the same conditions were performed, and the average OSPA distance and weight sum of 100 experiments was calculated. As shown in FIG. 12, FIG. 12- (1) shows the average OSPA distance over 100 time instants, and it can be seen that the OSPA value rises slightly in the immediate time instant, but the overall OSPA distance can always be controlled below 2. Fig. 12- (2) shows the average weighted sum over 100 time instants, and it can be seen that the weighted sum is rounded to 2, i.e. the number per time instant is estimated to be 2.
Experiment four: two object shape classifications
In the filtering updating method, the likelihood degree of the two shapes can be extracted better by utilizing the shape likelihood information. The method has great help to the tracking and classification of the target, and after useful target information is extracted by a filtering algorithm, the likelihood judgment is carried out on the target shape information and the preset shape information.
As shown in fig. 13, the classification result is a graph of shape information. Fig. 13- (1) shows the classification result at 100 times, and fig. 13- (2) shows that the shape fitting of the target at the new time is not enough, and the target is determined as another shape class before the likelihood with the shape information in the class information is low. In the shape classification after the filtering state extraction, the classification accuracy is evaluated, fig. 14 shows the average shape classification accuracy of 100 experiments, and it can be seen from the figure that the shape classification accuracy of the target at the time immediately adjacent is slightly reduced, but the accuracy is still maintained above 0.9.
Experiment five: target neogenesis
On the basis of two moving targets, a new target is set at the 60 th moment, falls into the monitoring areas of the two moving targets, and moves to the 100 th moment, and the new target keeps constant-speed linear motion.
As shown in fig. 15, fig. 15- (1) shows the classification results of three targets, and fig. 15- (2) shows the average classification accuracy obtained in 100 experiments. It can be seen that the new object moves at the beginning of the 60 th moment, and the shape is not well fitted, so that the category information of the new object cannot be accurately identified, and the accuracy rate is reduced. In the subsequent shape classification, the shape classification can be well divided. Fig. 16 shows the results of three target track traces. Fig. 17 and 18 show the average OSPA distance and the average weight sum of 100 experiments in the case of a targeted newborn by the filtering algorithm, respectively.
4. Quantitative analysis of the results of the experiment
In experiment one, verification of KDE-SSP method is carried out. In the method, only a plurality of determined peak points need to be found in the selection of candidate positions. Therefore, the method and the device can well divide the measurement set close to the target, and can control the time complexity in a lower range, so that the operation efficiency is higher.
In experiments two, three and four, the weights are updated by using likelihood calculation in the filtering updating algorithm respectively. When the target moves closely, the phenomenon of wrong tracking and missed tracking caused by inaccurate measurement set updating is easily caused by only using position updating. The shape information is added in the likelihood updating, the corresponding target state updating can be carried out on the measurement set, further the track information in the target motion can be effectively found out, and the target category can be accurately judged.
In experiment five, a nascent target was added to detect the effect on the method of the present application. As can be seen from the experimental result graph, the OSPA distance, the weight sum and the shape classification accuracy can be well adapted to the new situation with the target, and the algorithm has stronger stability.
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.

Claims (10)

1. The method for tracking and classifying the multi-extension target track based on the B spline shape drive is characterized by comprising the following steps:
establishing a B-Spline-GM-PHD filter, initializing a target state at the moment k:
Figure FDA0003571889500000011
wherein x iskRepresenting a state of motion, BkRepresenting B-spline shape information, pk,i、θk,iRespectively represents the polar diameter and polar angle information at the ith angle at the moment k, LkA tag representing the gaussian component track at time k,
Figure FDA0003571889500000012
a track label representing the jth Gaussian component at the time k;
when k is larger than or equal to 1, performing primary division on the measurement set by a distance division method;
if the divided measurement set has an adjacent target, performing secondary division on the divided measurement set, and if the divided measurement set does not have an adjacent target, performing multi-hypothesis filtering on the target state by using the B-Spline-GM-PHD filter;
performing multi-hypothesis filtering on the target state by adopting the B-Spline-GM-PHD filter to update the target state and obtain a shape likelihood function after weight updating;
fusing target components meeting a distance fusion threshold, pruning Gaussian components with undersized weights after fusion, and taking the Gaussian components with high weights as extraction targets in the fusion process, wherein the extraction targets comprise target motion states, target shape information and target tracks;
the likelihood is obtained by the shape likelihood function for the extracted target shape information and a preset target shape with category information, and target shape category information is obtained;
and if the observation information arrives at the next moment, re-executing the step of carrying out primary division on the measurement set by the distance division method, otherwise, ending the target track tracking process.
2. The method for tracking and classifying the multi-extension target track based on the B-spline shape driving according to claim 1, wherein the obtaining of the target shape category information by the likelihood of the extracted target shape information and the preset target shape with the category information by the shape likelihood function comprises:
set the preset target shape with the category information as
Figure FDA0003571889500000017
The set includes a preset' Y-shape
Figure FDA0003571889500000013
And a cross shape
Figure FDA0003571889500000014
Class information, wherein
Figure FDA0003571889500000015
Using all the extracted object shape information sets Bk|kEach shape of (a) is respectively associated with the preset target shape set
Figure FDA0003571889500000016
Using the shape likelihood function of the B-Spline-GM-PHD filter to obtain a formula
Figure FDA00035718895000000210
Solving for each extracted shape
Figure FDA0003571889500000021
All have the most likely shape to match
Figure FDA0003571889500000029
And when the likelihood exceeds a category threshold, classifying the s-th extracted shape into a c-th category, and when the likelihood is smaller than the category threshold, classifying the s-th extracted shape into other categories.
3. The method for tracking and classifying the target track based on the B-Spline shape driving multiple extensions according to claim 1 or 2, wherein the performing multiple hypothesis filtering on the target state by using the B-Spline-GM-PHD filter to update the target state and obtain the shape likelihood function after the weight update comprises:
and sequentially carrying out target prediction and target update of the B-Spline-GM-PHD filter on the target state, wherein a weight update formula considering the target is as follows:
Figure FDA0003571889500000022
Figure FDA0003571889500000023
Figure FDA0003571889500000024
wherein,
Figure FDA0003571889500000025
representing a position likelihood under a current metrology set, the position likelihood solved using a Gaussian distribution,
Figure FDA0003571889500000026
representing the shape likelihood function, the calculation process is as follows:
Figure FDA0003571889500000027
Figure FDA0003571889500000028
wherein HW、RWDenotes an innovation parameter, r'k|k-1,iIndicates the predicted pole diameter, r 'after treatment'W,iRepresenting the measured collector diameter after processing, and lambda represents a shape similarity mark;
there are two cases for the shape likelihood function, when r'k|k-1,i|＝|r′W,iIf not, the shape likelihood is calculated by the second formula.
4. The B-spline shape-driven multi-extended target track tracking and classifying method according to claim 3, wherein the processing method of the polar path comprises the following steps:
known predicted polar path information
Figure FDA0003571889500000031
Polar diameter information corresponding to the current measurement set
Figure FDA0003571889500000032
Two polar diameter information are divided into n1The dimension expansion is 360 dimensions, resulting in:
Figure FDA0003571889500000033
Figure FDA0003571889500000034
from the predicted polar diameter information r after dimension expansionk|k-1And measuring collector radius information r after dimension expansionWThe peak values of the wave crest and the wave trough obtained from the polar angle polar diameter diagram are respectively Pk|k-1And PWThe corresponding peak numbers are respectively
Figure FDA0003571889500000035
And
Figure FDA0003571889500000036
and by comparing the number of the peak values of the two, the predicted polar diameter information after dimension expansion and the measured collective polar diameter information after dimension expansion are respectively reordered, and the polar angle information is correspondingly changed.
5. The method for tracking and classifying the flight path of the multi-extended target based on the B-spline shape drive of claim 4, wherein the step of reordering the predicted polar path information after the dimension expansion and the measured collective polar path information after the dimension expansion by comparing the number of peaks of the two comprises the steps of:
(1) when in use
Figure FDA0003571889500000037
The method comprises the following steps:
case 1, match to two peak points:
Pk|k-1(1,1:2)≈PW(1,u0:v)
Figure FDA0003571889500000038
v＝mod(u0+1,Num),or v＝Num
r′k|k-1＝[rk|k-1(lk|k-1(1)),…,rk|k-1(end),rk|k-1(1),…,rk|k-1(lk|k-1(1)-1)]
r′W＝[rW(lW(u0)),…,rW(end),rW(1),…,rW(lW(u0)-1)]
λ＝1
case 2, matching to a peak point:
Pk|k-1(1)≈PW(u0)
Figure FDA0003571889500000039
r′k|k-1＝[rk|k-1(lk|k-1(1)),…,rk|k-1(end),rk|k-1(1),…,rk|k-1(lk|k-1(1)-1)]
r′W＝[rW(lW(u0)),…,rW(end),rW(1),…,rW(lW(u0)-1)]
Figure FDA00035718895000000310
case 3, no peak point matched:
Pk|k-1(1)≠PW(u0)
Figure FDA0003571889500000041
r′k|k-1＝rk|k-1
r′W＝rW
Figure FDA0003571889500000042
(2) when in use
Figure FDA0003571889500000043
The method comprises the following steps:
r′k|k-1＝rk|k-1
r′w＝rw
Figure FDA0003571889500000044
wherein "≈" indicates that the peak size of the predicted shape and the peak size of the metrology set shape are equal within a threshold range.
6. The method for tracking and classifying a multi-extended target track based on B-spline shape driving according to claim 1, wherein the secondarily partitioning the partitioned measurement sets comprises:
initializing a candidate shape parameter set C when k is 1:
Figure FDA0003571889500000045
wherein,
Figure FDA0003571889500000046
candidate shape parameters for fitting a B-spline curve,
Figure FDA0003571889500000047
respectively representing polar diameter and polar angle information at the ith angle; initializing polar angles
Figure FDA0003571889500000048
Is composed of
Figure FDA0003571889500000049
n1Is a predetermined dimension, and n1< 360, and the polar diameter information corresponding to the initialized polar angle is
Figure FDA00035718895000000410
When k is larger than or equal to 2, selecting m targets in the posterior target states, and putting shape information of the m posterior target states into the candidate shape parameter set C;
determining the central point position of each candidate shape in the candidate shape parameter set C by adopting a kernel density estimation method, and establishing a candidate central position set L:
Figure FDA00035718895000000411
wherein the coordinates corresponding to the density peak points of the candidate shape to which the B-spline curve is fitted are represented by (ω)q (x)，ωq (y)) N represents the number of density peak points;
the candidate shape parameter set C and the candidate center position set L are combined into
Figure FDA00035718895000000412
And adopting a shape selection division method to divide the combination to obtain an inner shape measurement subset and an outer shape measurement subset which are respectively as follows:
Figure FDA00035718895000000413
Figure FDA0003571889500000051
wherein,
Figure FDA0003571889500000052
a subset of the intra-shape measurements is represented,
Figure FDA0003571889500000053
representing a subset of extratopographic measurements, zkRepresenting the measurement points in the measurement set W,
Figure FDA0003571889500000054
represents the measurement point zkTo the central point position omegaqIs indicated by
Figure FDA0003571889500000055
The point corresponding to the point(s) of the image,
Figure FDA0003571889500000056
the included angle between the connecting line of the central point position and the h point on the shape and the x axis is shown,
Figure FDA0003571889500000057
an angle between a connecting line representing the position of the center point and the measuring point and the x-axis is set, and when the absolute value of the difference between the two is minimum, a point on the shape at the angle is selected as psi,
Figure FDA0003571889500000058
representing psi to center point position omegaqThe distance of (a);
Figure FDA0003571889500000059
the calculation formula of (a) is as follows:
Figure FDA00035718895000000510
Figure FDA00035718895000000511
Figure FDA00035718895000000512
wherein, bΨVector, p, representing the location of the center point to Ψk| k-1Coordinate information representing control vertices generated from the subset of intra-shape measurements, Ni,3(ψ) represents a B-spline basis function;
and respectively matching the shape internal measurement subset and the shape external measurement subset to obtain the likelihood, and obtaining the condition of the maximum weight product of the shape internal measurement subset and the shape external measurement subset.
7. The method for tracking and classifying tracks of multiple extended targets based on B-spline shape driving according to claim 6, wherein the matching the in-shape measurement subset and the out-shape measurement subset respectively for the likelihood to obtain the case of the maximum weight product of the in-shape measurement subset and the out-shape measurement subset comprises:
for intra-shape measurement subsets
Figure FDA00035718895000000513
Calculating likelihood by using the shape used when the shape is fitted and combined with the segmentation by using the intra-shape measurement subset, wherein the process of calculating the likelihood is a process of updating a shape likelihood function by using the B-S pline-GM-PHD filter;
for the out-of-shape measurement subset
Figure FDA00035718895000000514
Solving the weights by using the same process of updating the shape likelihood function, traversing all candidate shapes to respectively fit the shapes with the measurement set to obtain likelihood, and selecting the maximum value of the weights as the external measurement subset of the shapes
Figure FDA00035718895000000515
And finding the condition that the likelihood product of the shape internal measuring subset and the shape external measuring subset is maximum as the final segmentation result.
8. The method for tracking and classifying the multi-extended target track based on the B-spline shape driving according to claim 6, wherein the method for fitting the shape by adopting the B-spline curve comprises the following steps:
the B-spline curve is composed of a B-spline basis function Ni,k(u) and control vertex PiCollectively determined, the k-th order B-spline function is represented as:
Figure FDA0003571889500000061
wherein, Ni,k(u) is a k-1 degree B-spline basis function whose recurrence formula is:
Figure FDA0003571889500000062
wherein, U is { U ═1,u2,…,uk,…,un+kIs a set of node vectors for the B-spline;
when k is 3, the B-spline basis function is expressed as:
Figure FDA0003571889500000063
the process of obtaining the control vertex set in the B spline function by using the position information of the measuring points, wherein the measuring set of the extended target comprises a plurality of measuring points, comprises the following steps:
suppose that
Figure FDA0003571889500000066
Calculating the mean position of the measurement set for the measurement set of the extended target, establishing a coordinate system by taking the mean position as the origin of coordinates, dividing the coordinate system with the angle of 2 pi into n equal parts to obtain n control vertex positions, and expressing the control vertexes Lambda by polar coordinatesi＝{ρi,θi}，ρi、θiRespectively, the pole diameter and the pole angle, and the pole diameter is expressed as:
Figure FDA0003571889500000064
|Zi|＝{zk|d(zk,βi)＜λ1,k(zk,αi)＝1}
Figure FDA0003571889500000065
βi＝[-tan(θi)，1]
wherein, | · | represents the number of elements, d (z)k，βi) Represents the measurement point zkTo betaiDistance of straight line, λ1Distance threshold for constraint width, κ (z)k，αi) As a constraint, βiIs polar angle thetaiThe calculation formula of the determined vector, d (-) is as follows:
Figure FDA0003571889500000071
αi＝[1，tan(θi)]
wherein, | | · | | represents an absolute value, αiRepresentation and vector betaiA vector perpendicular to the straight line;
and through the calculation, solving a control vertex set Λ under a polar coordinate, converting the control vertex set Λ into a corresponding set P under a rectangular coordinate system, and generating a smooth curve related to the shape of the measurement set under the combined action of the control vertex set Λ and the B spline basis function.
9. The B-Spline shape-driven multi-expansion target track tracking and classifying method according to claim 3, wherein the prediction step of the B-Spline-GM-PHD filter is represented as:
Figure FDA0003571889500000072
Figure FDA0003571889500000073
Figure FDA0003571889500000074
Lk|k-1＝Lk-1∪Lβ
wherein,
Figure FDA0003571889500000075
representing the probability hypothesis density of the predicted target, Jk|k-1Represents the number of predicted gaussian components at the moment k,
Figure FDA0003571889500000076
respectively representing the weight, mean and covariance matrix of the jth Gaussian component predicted at the time k, N (-) is Gaussian distribution, Lk|k-1The label set representing the prediction at the time k comprises the label sets of the original target and the new target at the time k-1, and the predicted polar diameter and polar angle information are consistent with the posterior target state at the time k-1;
mean value
Figure FDA0003571889500000077
Sum covariance matrix
Figure FDA0003571889500000078
The predictions of (a) are respectively expressed as:
Figure FDA0003571889500000079
Figure FDA00035718895000000710
wherein, FK|k-1Representing a state transition matrix, IdRepresenting a d-dimensional identity matrix, QkRepresenting the process noise covariance matrix.
10. The method for tracking and classifying the track of the target based on the B-Spline shape driving multi-extension target of claim 3, wherein the updating step of the B-Spline-GM-PHD filter is expressed as:
Figure FDA00035718895000000711
wherein, the probability hypothesis density of the missed detection target is expressed as:
Figure FDA0003571889500000081
Figure FDA0003571889500000082
Figure FDA0003571889500000083
Figure FDA0003571889500000084
Figure FDA0003571889500000085
Figure FDA00035718895000000817
Lk|k＝Lk|k-1
wherein, γjA desired value representing the number of measurements made by the target,
Figure FDA0003571889500000086
representing the target detection probability:
the probability hypothesis density for target detection at time k is given by:
Figure FDA0003571889500000087
Figure FDA0003571889500000088
Figure FDA0003571889500000089
Figure FDA00035718895000000810
Figure FDA00035718895000000811
Figure FDA00035718895000000812
Figure FDA00035718895000000813
where σ is expressed as a preset forgetting factor, ρW，iFor the polar diameter information at the ith polar angle of the shape to which the current measurement set is fitted,
Figure FDA00035718895000000814
the sum of the measurement set numbers in all the division methods is represented, and each innovation parameter is represented as:
Figure FDA00035718895000000815
Figure FDA00035718895000000816
Figure FDA0003571889500000091
Figure FDA0003571889500000092
Figure FDA0003571889500000093
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