CN106468771B - A kind of multi-target detection and tracking method under high clutter conditions of low Observable - Google Patents

A kind of multi-target detection and tracking method under high clutter conditions of low Observable Download PDF

Info

Publication number
CN106468771B
CN106468771B CN201610835472.8A CN201610835472A CN106468771B CN 106468771 B CN106468771 B CN 106468771B CN 201610835472 A CN201610835472 A CN 201610835472A CN 106468771 B CN106468771 B CN 106468771B
Authority
CN
China
Prior art keywords
target
measurement
probability
llr
parameter
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.)
Expired - Fee Related
Application number
CN201610835472.8A
Other languages
Chinese (zh)
Other versions
CN106468771A (en
Inventor
唐续
吴骐
朱士强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201610835472.8A priority Critical patent/CN106468771B/en
Publication of CN106468771A publication Critical patent/CN106468771A/en
Application granted granted Critical
Publication of CN106468771B publication Critical patent/CN106468771B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/66—Sonar tracking systems
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
    • G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/523—Details of pulse systems
    • G01S7/526—Receivers
    • G01S7/527—Extracting wanted echo signals
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/534—Details of non-pulse systems
    • G01S7/536—Extracting wanted echo signals

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

Multi-target detection and tracking method under a kind of low high clutter conditions of Observable of the disclosure of the invention, belongs to radar and sonar technique field.Thinking of the invention is, when related question between processing target-measurement, consider to reach multiple measurements of receiver by different propagation paths for the measurement of possible target, and these are measured and is correctly associated with known each multipath measurement function respectively, to obtain the accumulation of target information, enhance target detection capabilities；Then target following is carried out by way of sliding window.Present invention utilizes the target informations that sensor is reached by different approaches, and these measurement informations are measured function with known each multipath respectively and are correctly associated with, to obtain the accumulation of target information, enhance target in low Observable, detectability under high clutter environment can effectively reduce the influence between adjacent target.

Description

A kind of multi-target detection and tracking method under high clutter conditions of low Observable
Technical field
The invention belongs to radars and sonar technique field, relate generally to maximum likelihood-probability multiple hypotheis tracking (ML-PMHT) Extended method.
Technical background
Target following technology is widely used in each field, especially radar (sonar) signal system.And target following skill Art tracks tracking (TBD) two major classes before (TAD) and detection after being divided into detection, and in comparison, TAD algorithm calculation amount is lower, benefit In real-time implementation, but since TAD algorithm depends on detection of the front end signal processor to target, in low signal-to-noise ratio (SNR) situation Lower tracking performance is undesirable.TBD algorithm is due to joined target detection while tracking, to target under low signal-to-noise ratio There is stronger tracking ability, but causes TBD algorithm to be applied in engineering by the restriction of calculation amount and much limited.
TBD algorithm realizes that the basic thought of target detection is, according to known observation function, can establish measurement with it is possible The likelihood function that dbjective state parameter is constituted.Bigger likelihood value will be obtained than clutter derived from the observation of target, and then In engineer application, generally require to initialize track before target tracking algorism implementation, to find the initial of target State vector, so that further progress tracks, in batch processing TBD algorithm, maximum likelihood-probability multiple hypotheis tracking (ML- PMHT) algorithm is a kind of effective and complete object detecting and tracking algorithm.
The most important basic assumption of ML-PMHT algorithm: in same frame, measurement is independent of each other with target association, i.e., It can one target of multiple measurements associations.Based on this it is assumed that ML-PMHT is very easy to expand to the situation of multiple target, and count Calculation amount linearly increases with destination number.ML-PMHT is carried out based on the total log-likelihood ratio (LLR) obtained to multiframe observation data It maximizes, exports corresponding state parameter vector after obtaining LLR maximum value based on searching algorithm after obtaining LLR table and reaching formula, often Searching algorithm has: grid data service (MPG), genetic search algorithm (GA) and based on observation space reflection be mapped to parameter space Direct subspace search method (DSS).Existing basic ML-PMHT algorithm carries out LLR using a fixed likelihood function It calculates.When, there are multipath, there are multiple derived from same target and through difference in the frame data received for observer in environment The measurement that path reaches, it is clear that respectively derived from the measurement of same target all containing the information of target.However, these are measured and target Observation function relationship before state is different, and is carried out calculating resulting LLR with a fixed likelihood function and not only cannot Target information is accumulated, also will form false dbjective state estimates of parameters.In addition, existing basic ML-PMHT algorithm pair Multiple target is by the way of Sequence Detection, and when adjacent to one another between target, a target is searched, other targets are simultaneously It is not easy to be found.
The target following of ML-PMHT algorithm is executed in a manner of sliding window, i.e., can be by searching in each sliding window Rope algorithm obtains a state estimation, then the certain frame number of sliding window forward slip, is obtained down again by Local Optimization Algorithm One state estimation.These state point sets can be obtained by the track of target by track management program.
Summary of the invention
The deficiency applied in multi-path environment it is an object of the invention to extend traditional ML-PMHT algorithm, proposes a kind of benefit The dim multitarget detection and tracking system observed with multipath, can correctly estimate the faint multiple target kinematic parameter in multi-path environment This method is known as joint maximum likelihood-multipath-probability multiple hypotheis tracking (JML-MP-PMHT) algorithm.Meanwhile proposing a kind of more mesh Joint initial method is marked, target detection and state initialization are carried out with multiple target formula, can be effectively reduced between adjacent target Influence.
Thinking of the invention is, when related question between processing target-measurement, consideration is arrived by different propagation paths Multiple measurements up to receiver are the measurement of possible target, and these measurements are correct with known each multipath measurement function respectively Association enhances target detection capabilities to obtain the accumulation of target information.Then carried out by way of sliding window target with Track.
The technical scheme is that a kind of multi-target detection and tracking method under high clutter conditions of low Observable, the party Method includes:
Step 1: initialization；
1a. initialization observing environment parameters include: slant range variance, orientation variance, Doppler variance, and false-alarm is general Rate, detection probability, clutter density λ, sampling interval verify thresholding, monitoring space V；
1b. imports observation information: including sharing T frame data, having N in each sliding windowwFrame data, the sliding window is interior to be measured Data acquisition systemI-th frame amount measured data set Z (i), the i-th frame amount survey collective number miReceiver is reached with L kind propagation path Measurement model；
(2) the LLR calculation formula of JML-MP-PMHT is constructed:
The most important basic assumption of 2a. is any measurement zj(i) ∈ Z (i) at most passes through a kind of propagation road by a target Diameter generates, and a target can generate any amount of measurement by a kind of propagation path；
2b. suppose there is K target xK=[x1,x2,...,xK], then define prior probability indicate a measurement source in A possibility that some propagation path of some target, calculation formula is usual are as follows:
WhereinFor target xkPassage path l generates the detection probability measured；
The calculation formula of 2c.LLR value are as follows:
Wherein pl(zj(i)|xk) indicate target xkPassage path l, which is generated, measures zj(i) likelihood function:
Wherein xtFor transmitter state, xrFor receiver state, hl() is measurement model corresponding to l kind path, Rl It (i) is the covariance matrix of measurement model corresponding to l kind path；
(3) N in sliding window is initializedwFrame data and metric data setIt has been known that there is K targets to exist；Work as K=0 When, directly execution step (8)；Otherwise it performs the next step；
(4) to already present K targeted packets；Purpose is will to gather one piece apart from close target, will be apart from remote mesh Mark separates；Its rule of classification is χ2It examines:
(xm-xn)T(Cm+Cn)-1(xm-xn)≤γ
Wherein Cm、CnRespectively indicate target xm、xnState covariance, γ be predefined thresholding；
(5) the multiple target state in each grouping carries out local optimum with JML-MP-PMHT LLR formula；
(6) existence verification is carried out to each target:
6a. initializing variable k=K
6b. selected target xk, other target collections are xK-1=[x1,...,xk-1,xk+1,...,xK]
6c. has K-1 other existing dbjective states, has complete metric data set in sliding windowDefine frame sequence Number variable i=1；
6d. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path l
This makes it possible to obtain three-dimensional probability matrix [L, K-1, a mi]；
6e. takes the greatest member value in probability matrixA given probability threshold if more than then will The third dimension is equal in three-dimensional matriceAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having Probability value is greater than probability threshold；
6f. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, It performs the next step；Otherwise i=i+1 returns to step 6d；
The remaining measurement of 6g. forms new measurement setThen it calculates and there was only target xkJML-MP-PMHT LLR Formula；If the LLR value is greater than verification thresholding, target xkIn the presence of；Otherwise target xkIt is not present, deletes the target, and K=K- 1；
If 6h. k=1, performs the next step；Otherwise k=k-1 returns to step 6b；
(7) it suppose there is K*A target has passed through existence verification, enables K=K*, that is, there is K target；
(8) fresh target is searched for；
Enable K*=K+1 solves the LLR formula of JML-MP-PMHT using multipath-directly subspace search (MD-DDS) method Global maximal solution, state parameter corresponding to global maximal solution is the fresh target init state estimated；
(9) to fresh target carry out thresholding verification, verify its whether necessary being:
9a. has K existing dbjective states, has complete metric data set in sliding windowDefine frame number variable I=1；
9b. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path lObtain three-dimensional probability matrix [L, K, a mi]；
9c. takes the greatest member value in probability matrixA given probability threshold if more than then will The third dimension is equal in three-dimensional matriceAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having Probability value is greater than probability threshold；
9d. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, It performs the next step；Otherwise i=i+1 returns to step 9b；
The remaining measurement of 9e. forms new measurement setThen the JML-MP-PMHT LLR for there was only fresh target is calculated Formula.If the LLR value is greater than verification thresholding, fresh target exists, and enables K=K*, return step (8) continues to search for fresh target； Otherwise fresh target verifying is not present, and performs the next step；
(10) judge whether sliding window includes the last N of data setwFrame data, if not provided, sliding window forward slip is certain Sampling interval forms N in new windowwFrame data and metric data setReturn to step (3)；Otherwise method terminates.
Further, the step 8 method particularly includes:
Free parameter grid is arranged in 8a.；
State parameter spaceIn, [ρ (i) b (i)] indicate ground coordinate under radial distance and Angle,Indicate radial distance change rate and angle variable rate；And it measures space [Rg Rr Az] and respectively indicates sight Measurement: slant range, Doppler, tilt angle；It can only be determined from space reflection is measured to parameter spaceThree A parameter, so claimingFor free parameter；In parameter space, free parameter is divided into grid, each mesh point is corresponding OneParameter；Frame number variable i=1 is defined simultaneously；
8b. is by each measurement z of the i-th frame dataj(i) parameter space is all changed to by L kind observation model inversion；
State parameter is to pass through h by l kind measurement model to the conversion measuredl() measurement equation, then measure inversion State parameter is changed to need to be inversely transformed into h to l kind measurement equationl(·)-1；Due to there is miA metric data and L kind measure mould Type, then measuring inversion and changing to the number of parameter space is miL value；
8c. is based on range information, by this miL location point is clustered, and the cluster of most location points is chosen；If maximum Only one element in cluster then skips the step and directly executes step 8e, because this frame data is likely to be generated by clutter , without target information, therefore calculation amount can be reduced by ignoring；Otherwise the maximum mean value clustered is sought
8d. is by mean location pointThe free parameter of joint step 8a settingMesh point has been formed Whole state parameter mesh pointIn conjunction with K dbjective state having been found that, substituting into prediction has K* LLR value is calculated in the JML-MP-PMHT LLR formula of a target；
8e. judges i=NwIt is whether true, if set up, perform the next step；Otherwise i=i+1 returns to step 8b；
8f. takes the maximum value in all LLR values, by the state parameter corresponding to it be transmitted to local optimization process carry out it is excellent Change, must solve.
The present invention has the following advantages:
First, present invention utilizes the target informations that sensor is reached by different approaches, and these measurement informations point It does not measure function with known each multipath to be correctly associated with, to obtain the accumulation of target information, enhancing target is in low Observable, height Detectability under clutter environment,
Second, the present invention realizes a kind of multiple target joint initial method, with multiple target formula carry out target detection and State initialization can effectively reduce the influence between adjacent target.
Third, the present invention carry out Target state estimator by the batch processing mode of sliding window, carry out track by these point sets Management, to realize multiple target tracking.
Detailed description of the invention
Fig. 1 is the position and measurement model geometric graph of target and sensor under over-the-horizon radar.
Fig. 2 is under two kinds of ionospheres E and F, and signal is from transmitter sensor to target again to the propagation of receiver sensor Path profile.Respectively this 4 kinds of propagation paths of EE, EF, FE and FF, corresponding 4 kinds of measurement models.
Fig. 3-5 is respectively the slant range of lower 35 sampling instants of 5 target environments, Doppler, tilt angle observation. In figure: clutter is indicated with asterisk, is indicated by the measurement that 4 kinds of propagation paths generate with square by target 1, similarly, is derived from The measurement of target 2 indicates that the measurement from target 3 is indicated with five-pointed star with diamond shape, from the measurement triangle of target 4 It indicates, the measurement from target 5 is indicated with circle.
Fig. 6 is JML-MP-PMHT algorithm multiple target tracking result figure under 5 target environments.
Specific embodiment
It to make the objectives, technical solutions, and advantages of the present invention clearer, below will be to specific embodiment party of the invention Formula is described in further detail.
(1) initial background parameter.
In over-the-horizon radar application scenarios, receiver sensor is fixed on [0km, 0km] and collects by ionospheric reflection 1a. Signal back, transmitter sensor are fixed on [100km, 0km].It is assumed that there are two ideal ionosphere E and F as shown in Figure 1, They are corresponding, and there are two fixed height hE=100km and hF=220km, then signal from transmitter sensor to target again There are EE, EF, FE and FF totally 4 kinds of propagation paths to receiver sensor.35 sampling instants, JML- have been observed in the scene altogether The sliding window of MP-PMHT algorithm includes 10 sampling instants, i.e. 10 frame data execute 1 frame data of sliding window forward slip every time. Wherein in sampling process, there are 5 targets to be divided into the motion vector with original state are as follows:
x1=[1100km 0.15km/s 0.10472rad 8.72665e-5rad/s],
x2=[1130km 0.15km/s 0.10472rad 8.72665e-5rad/s],
x3=[1260km -0.15km/s 0.10472rad 8.72665e-5rad/s],
x4=[1200km -0.15km/s 0.10472rad 8.72665e-5rad/s],
x5=[1220km 0.15km/s 0.10472rad 8.72665e-5rad/s],
Do linear uniform motion, and the current moment that goes out of this 5 targets is respectively [1,1,1,10,1], the moment that disappears be [35, 35,35,35,20].The track of target and sensor is as shown in Figure 2.
It is assumed that the echo amplitude that receives of sensor is in rayleigh distributed, then corresponding detection probability PdWith false-alarm probability PFA's Calculation formula is as follows:
In formula, d is the signal-to-noise ratio for monitoring environment, and Th is detection threshold of the sensor to echo.
Wherein sensor parameters in scene, slant range resolution cell size CRg, Doppler's resolution cell size CRrWith incline Rake angle resolution cell size CAzRespectively 17.3205km, 0.0035km/s and 0.0104rad.By different propagation path EE, The SNR value of EF, FE and FF are respectively [5dB, 4dB, 4dB, 5dB], Th=2.70, then PFA=0.026, corresponding each path detection Probability [0.42,0.35,0.35,0.42].
It is then assumed that clutter is uniformly distributed in unit, then measuring standard difference is respectively as follows:
The observation data that 1b. sensor receives are as in Figure 3-5.After JML-MP-PMHT algorithm environment parameter determines, also Determine observation model.From state of ground parameter coordinateTo sensor observation coordinate [Rg Rr Az] Mapping be observation model can be obtained by the geometrical model of Fig. 2:
η=ρ-dsin (b)
Rg=r1+r2
Az=sin-1{ρsin(b)/(2r1)}
Wherein hrAnd htIt is substituted for the height of ionosphere E and F, as 4 kinds of different observation models respectively.Correspondingly, from sight Survey the inverse mapping of coordinate to state parameter coordinate are as follows:
r2=Rg-r1
(2) JML-MP-PMHT LLR calculation formula is constructed:
(3) N in sliding window is initializedw=10 frame data and metric data setIt has been known that there is K targets to exist.When When K=0, directly execution step (8)；Otherwise it performs the next step；
(4) to already present K targeted packets.Purpose is will to gather one piece apart from close target, will be apart from remote mesh Mark separates.Its rule of classification is χ2It examines:
(xm-xn)T(Cm+Cn)-1(xm-xn)≤γ
Wherein Cm、CnRespectively indicate target xm、xnState covariance, γ be predefined thresholding；
(5) the multiple target state in each grouping carries out local optimum with JML-MP-PMHT LLR formula；
(6) existence verification is carried out to each target:
6a. initializing variable k=K
6b. selected target xk, other target collections are xK-1=[x1,...,xk-1,xk+1,...,xK]
6c. has K-1 other existing dbjective states, has complete metric data set in sliding windowDefine frame Serial number variable i=1；
6d. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path l
This makes it possible to obtain three-dimensional probability matrix [L, K-1, a mi]；
6e. takes the greatest member value in probability matrixA given probability threshold if more than then will The third dimension is equal in three-dimensional matriceAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having Probability value is greater than probability threshold；
6f. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, It performs the next step；Otherwise i=i+1 returns to step 6d；
The remaining measurement of 6g. forms new measurement setThen it calculates and there was only target xkJML-MP-PMHT LLR Formula.If the LLR value is greater than verification thresholding, target xkIn the presence of；Otherwise target xkIt is not present, deletes the target, and K=K- 1；
If 6h. k=1, performs the next step；Otherwise k=k-1 returns to step 6b.
(7) it suppose there is K*A target has passed through existence verification, enables K=K*, that is, there is K target；
(8) fresh target is searched for.Enable K*=K+1 solves JML- using multipath-directly subspace search (MD-DDS) method The global maximal solution of MP-PMHT LLR formula, state parameter corresponding to global maximal solution are that the fresh target estimated is initial Change state
Free parameter grid is arranged in 8a..In parameter space, free parameter is drawnIt is divided into grid.Frame sequence is defined simultaneously Number variable i=1；
8b. is by each measurement z of the i-th frame dataj(i) parameter space is all changed to by L=4 kind observation model inversion.Shape State parameter is to pass through h by l kind measurement model to the conversion measuredl() measurement equation then measures inversion and changes to state ginseng Number needs to be inversely transformed into h to l kind measurement equationl(·)-1, i.e. inverse mapping in step 1b.Due to there is miA metric data and L Kind measurement model, then measuring inversion and changing to the number of parameter space is miL value；
8c. is based on range information, by this miL location point is clustered, and the cluster of most location points is chosen.If maximum Only one element in cluster then skips the step and directly executes step 8e, because this frame data is likely to be generated by clutter , without target information, therefore calculation amount can be reduced by ignoring；Otherwise the maximum mean value clustered is sought
8d. is by mean location pointThe free parameter of joint step 8a settingMesh point has been formed Whole state parameter mesh pointIn conjunction with K dbjective state having been found that, substituting into prediction has K* LLR value is calculated in the JML-MP-PMHT LLR formula of a target；
8e. judges i=NwIt is whether true, if set up, perform the next step；Otherwise i=i+1 returns to step 8b；
8f. takes the maximum value in all LLR values, by the state parameter corresponding to it be transmitted to local optimization process carry out it is excellent Change, must solve；
(9) to fresh target carry out thresholding verification, verify its whether necessary being:
9a. has K existing dbjective states, has complete metric data set in sliding windowFrame number is defined to become Measure i=1；
9b. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path lObtain three-dimensional probability matrix [L, K, a mi]；
9c. takes the greatest member value in probability matrixA given probability threshold if more than then will The third dimension is equal in three-dimensional matriceAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having Probability value is greater than probability threshold；
9d. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, It performs the next step；Otherwise i=i+1 returns to step 9b；
The remaining measurement of 9e. forms new measurement setThen the JML-MP-PMHT LLR for there was only fresh target is calculated Formula.If the LLR value is greater than verification thresholding, fresh target exists, and enables K=K*, return step (8) continues to search for fresh target； Otherwise fresh target verifying is not present, and performs the next step；
(10) judge whether sliding window includes the last N of data setwFrame data, if not provided, 1 frame number of sliding window forward slip According to interval, form N in new windowwFrame data and metric data setReturn to step (3)；Otherwise method terminates.
In this example implementation, solid line is the track result to multiple target tracking 200 times in Fig. 6.The result shows that tracking Track and the true track of target are very close, and it is effective for carrying out the multiple target tracking under multi-path environment with ML-MP-PMHT.
Finally, it is stated that the above implementation is only used to illustrate the technical scheme of the present invention and not to limit it, it is all according to Shen of the present invention Please the scope of the patents equivalent change and modification done, be all covered by the present invention.

Claims (2)

1. a kind of multi-target detection and tracking method under high clutter conditions of low Observable, this method comprises:
Step 1: initialization；
1a. initialization observing environment parameters include: slant range variance, orientation variance, Doppler variance, false-alarm probability, Detection probability, clutter density λ, sampling interval verify thresholding, monitoring space V；
1b. imports observation information: including sharing T frame data, having N in each sliding windowwFrame data, metric data in the sliding window SetI-th frame amount measured data set Z (i), the i-th frame amount survey collective number miThe amount of receiver is reached with L kind propagation path Survey model；
Step 2: the calculation formula of tectonic syntaxis maximum likelihood-multipath-probability multiple hypotheis tracking algorithm:
2a. hypothesis is any measurement zj(i) ∈ Z (i) is at most generated by a target by a kind of propagation path, and a target Any amount of measurement can be generated by a kind of propagation path；
2b. suppose there is K target xK=[x1,x2,...,xK], then defining prior probability indicates a measurement source in some mesh A possibility that some propagation path of target, calculation formula is usual are as follows:
WhereinFor target xkPassage path l generates the detection probability measured；
The calculation formula of 2c.LLR value are as follows:
Wherein pl(zj(i)|xk) indicate target xkPassage path l, which is generated, measures zj(i) likelihood function:
Wherein xtFor transmitter state, xrFor receiver state, hl() is measurement model corresponding to l kind path, Rl(i) it is The covariance matrix of measurement model corresponding to l kind path；
Step 3: the N in initialization sliding windowwFrame data and metric data setIt has been known that there is K targets to exist；Work as K=0 When, directly execute step 8；Otherwise it performs the next step；
Step 4: already present K targeted packets；Purpose is will to gather one piece apart from close target, will be apart from remote target point Every；Its rule of classification is χ2It examines:
(xm-xn)T(Cm+Cn)-1(xm-xn)≤γ
Wherein Cm、CnRespectively indicate target xm、xnState covariance, γ be predefined thresholding；
Step 5: the multiple target state in each grouping is public with joint maximum likelihood-multipath-probability multiple hypotheis tracking algorithm LLR Formula carries out local optimum；
Step 6: existence verification is carried out to each target:
6a. initializing variable k=K
6b. selected target xk, other target collections are xK-1=[x1,...,xk-1,xk+1,...,xK]
6c. has K-1 other existing dbjective states, has complete metric data set in sliding windowFrame number is defined to become Measure i=1；
6d. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path l
This makes it possible to obtain three-dimensional probability matrix [L, K-1, a mi]；
6e. takes the greatest member value in probability matrixA given probability threshold if more than, then will be three-dimensional The third dimension is equal in matrixAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*'s All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having probability Value is greater than probability threshold；
6f. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, execute In next step；Otherwise i=i+1 returns to step 6d；
The remaining measurement of 6g. forms new measurement setThen it calculates and there was only target xkJoint maximum likelihood-multipath-it is general The LLR formula of rate multiple hypotheis tracking algorithm；If the LLR value is greater than verification thresholding, target xkIn the presence of；Otherwise target xkIt does not deposit Deleting the target, and K=K-1；
If 6h. k=1, performs the next step；Otherwise k=k-1 returns to step 6b；
Step 7: suppose there is K*A target has passed through existence verification, enables K=K*, that is, there is K target；
Step 8: search fresh target；
Enable K*=K+1 solves joint maximum likelihood-multipath-probability multiple hypotheis tracking using multipath-directly subspace search method The global maximal solution of the LLR formula of algorithm, state parameter corresponding to global maximal solution is the fresh target initialization estimated State；
Step 9: to fresh target carry out thresholding verification, verify its whether necessary being:
9a. has K existing dbjective states, has complete metric data set in sliding windowDefinition frame number variable i= 1；
9b. is calculated in the i-th frame amount measured data and is measured zj(i) target x is derived fromkWith the posteriority association probability of path l? To three-dimensional probability matrix [L, K, a mi]；
9c. takes the greatest member value in probability matrixA given probability threshold if more than, then will be three-dimensional The third dimension is equal in matrixAll elements, that is, two-dimensional matrixIt is equal to l with the first dimension*And second dimension be equal to k*'s All elements, that is, one-dimensional matrix [l*,k*:] and it is set to 0, record the measurement serial numberThe step is executed repeatedly, until not having probability Value is greater than probability threshold；
9d. rejects these greater than metric data corresponding to probability threshold, judges i=NwIt is whether true, if set up, execute In next step；Otherwise i=i+1 returns to step 9b；
The remaining measurement of 9e. forms new measurement setThen it calculates and only has joint maximum likelihood-multipath-of fresh target general The LLR formula of rate multiple hypotheis tracking algorithm；If the LLR value is greater than verification thresholding, fresh target exists, and enables K=K*, return to step Rapid 8 continue to search for fresh target；Otherwise fresh target verifying is not present, and performs the next step；
Step 10 judges whether sliding window includes the last N of data setwFrame data, if not provided, sliding window forward slip centainly samples Interval, forms N in new windowwFrame data and metric data setReturn to step 3；Otherwise method terminates.
2. the multi-target detection and tracking method under a kind of low high clutter conditions of Observable as described in claim 1, feature It is the step 8 method particularly includes:
Free parameter grid is arranged in 8a.；
State parameter spaceIn, [ρ (i) b (i)] indicates radial distance and angle under ground coordinate,Indicate radial distance change rate and angle variable rate；And it measures space [Rg RrAz] and respectively indicates observed quantity: Slant range, Doppler, tilt angle；It can only be determined from space reflection is measured to parameter spaceThree ginsengs Number, so claimingFor free parameter；In parameter space, free parameter is divided into grid, each mesh point is one correspondingParameter；Frame number variable i=1 is defined simultaneously；
8b. is by each measurement z of the i-th frame dataj(i) parameter space is all changed to by L kind observation model inversion；
State parameter is to pass through h by l kind measurement model to the conversion measuredl() measurement equation then measures inversion and changes to shape State parameter needs to be inversely transformed into h to l kind measurement equationl(·)-1；Due to there is miA metric data and L kind measurement model, then measure Surveying inversion and changing to the number of parameter space is miL value；
8c. is based on range information, by this miL location point is clustered, and the cluster of most location points is chosen；If maximum cluster In only one element, then skip the step and directly execute step 8e because this frame data be likely to by clutter generate, do not have There is target information, therefore calculation amount can be reduced by ignoring；Otherwise the maximum mean value clustered is sought
8d. is by mean location pointThe free parameter of joint step 8a settingMesh point is formed complete State parameter mesh pointIn conjunction with K dbjective state having been found that, substituting into prediction has K*A mesh LLR value is calculated in target JML-MP-PMHT LLR formula；
8e. judges i=NwIt is whether true, if set up, perform the next step；Otherwise i=i+1 returns to step 8b；
8f. takes the maximum value in all LLR values, and the state parameter corresponding to it is transmitted to local optimization process and is optimized, is obtained Solution.
CN201610835472.8A 2016-09-21 2016-09-21 A kind of multi-target detection and tracking method under high clutter conditions of low Observable Expired - Fee Related CN106468771B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610835472.8A CN106468771B (en) 2016-09-21 2016-09-21 A kind of multi-target detection and tracking method under high clutter conditions of low Observable

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610835472.8A CN106468771B (en) 2016-09-21 2016-09-21 A kind of multi-target detection and tracking method under high clutter conditions of low Observable

Publications (2)

Publication Number Publication Date
CN106468771A CN106468771A (en) 2017-03-01
CN106468771B true CN106468771B (en) 2019-01-11

Family

ID=58230668

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610835472.8A Expired - Fee Related CN106468771B (en) 2016-09-21 2016-09-21 A kind of multi-target detection and tracking method under high clutter conditions of low Observable

Country Status (1)

Country Link
CN (1) CN106468771B (en)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107219498B (en) * 2017-05-22 2019-08-27 杭州电子科技大学 The passive co-located method of more base station single frequency network based on MML-PMHT
CN107526070A (en) * 2017-10-18 2017-12-29 中国航空无线电电子研究所 The multipath fusion multiple target tracking algorithm of sky-wave OTH radar
CN108363054B (en) * 2018-02-07 2021-07-02 电子科技大学 Passive radar multi-target tracking method for single-frequency network and multi-path propagation
CN111611989B (en) * 2020-05-22 2023-08-01 四川智动木牛智能科技有限公司 Multi-target accurate positioning identification method based on autonomous robot
CN111679270B (en) * 2020-05-26 2022-05-03 电子科技大学 Multipath fusion target detection algorithm under scene with uncertain reflection points
CN112731373B (en) * 2020-12-24 2023-09-22 西安理工大学 External radiation source radar multi-target tracking method based on three-dimensional data association
CN112285698B (en) * 2020-12-25 2021-04-20 四川写正智能科技有限公司 Multi-target tracking device and method based on radar sensor
CN112946624B (en) * 2021-03-01 2023-06-27 西安交通大学 Multi-target tracking method based on track management method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6999599B2 (en) * 2002-06-07 2006-02-14 Microsoft Corporation System and method for mode-based multi-hypothesis tracking using parametric contours
WO2012149624A1 (en) * 2011-05-04 2012-11-08 Jacques Georgy Two-stage filtering based method for multiple target tracking
CN104021519B (en) * 2014-06-17 2017-10-13 电子科技大学 Multiple-moving target tracking method under the conditions of dense clutter based on GPU architecture
CN104268567A (en) * 2014-09-18 2015-01-07 中国民航大学 Extended target tracking method using observation data clustering and dividing
CN105445732B (en) * 2015-11-25 2017-10-17 电子科技大学 The targetpath initial method observed under the conditions of dense clutter using multipath

Also Published As

Publication number Publication date
CN106468771A (en) 2017-03-01

Similar Documents

Publication Publication Date Title
CN106468771B (en) A kind of multi-target detection and tracking method under high clutter conditions of low Observable
CN106842165B (en) Radar centralized asynchronous fusion method based on different distance angular resolutions
Daun et al. Tracking in multistatic passive radar systems using DAB/DVB-T illumination
Kellner et al. Instantaneous lateral velocity estimation of a vehicle using Doppler radar
US10567918B2 (en) Radio-location method for locating a target device contained within a region of space
US12080060B2 (en) Method and system for indoor multipath ghosts recognition
CN105445732B (en) The targetpath initial method observed under the conditions of dense clutter using multipath
US11506745B2 (en) Vehicular self-positioning
CN108363054A (en) Passive radar multi-object tracking method for Single Frequency Network and multipath propagation
CN109001706B (en) Passive direct positioning method for multiple radiation source targets based on characteristic value maximization
Zhao et al. Comparing centralized Kalman filter schemes for indoor positioning in wireless sensor network
Alhashimi et al. Bfar-bounded false alarm rate detector for improved radar odometry estimation
CN104101861B (en) Distance-measuring and positioning method and system
Blanding et al. ML-PDA: Advances and a new multitarget approach
CN106772357B (en) AI-PHD filter multi-object tracking method under signal-to-noise ratio unknown condition
CN113608193A (en) Radar multi-target distance and speed estimation method based on UNet
Daun et al. Track initialization in a multistatic DAB/DVB-T network
Parmar et al. Multiple target tracking and data association in wireless sensor network
Tang et al. Multipath maximum likelihood probabilistic multihypothesis tracker for low observable targets
CN106093891A (en) Radar fence anti-crowding measure false target jamming profile method based on doppler velocity inspection
Uney et al. Maximum likelihood signal parameter estimation via track before detect
KR101356019B1 (en) Position estimating method and system on reducing estimated positioning error for bi-static radar system based on cellular network
Yang et al. Human Intrusion Detection System using mm Wave Radar
Liu et al. Design and performance of an integrated waveform-agile multi-modal track-before-detect sensing system
CN117241221B (en) Indoor positioning method based on uncertainty learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20190111

Termination date: 20210921

CF01 Termination of patent right due to non-payment of annual fee