CN108919255A - Gao Zhongying radar weak target detection tracking based on CS-PF - Google Patents
Gao Zhongying radar weak target detection tracking based on CS-PF Download PDFInfo
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- CN108919255A CN108919255A CN201810874680.8A CN201810874680A CN108919255A CN 108919255 A CN108919255 A CN 108919255A CN 201810874680 A CN201810874680 A CN 201810874680A CN 108919255 A CN108919255 A CN 108919255A
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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
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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
- 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
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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Abstract
The invention discloses a kind of Gao Zhongying radar weak target detection tracking based on CS-PF, belongs to radar data process field.There are an apparent defects for Dim targets detection tracking based on PF-TBD, i.e. when radar uses the operating mode of Gao Zhongying, there are the fuzzy of height for its range measurement to target, there is serious accumulation mistake to Weak target cumulative process so as to cause PF-TBD method, it can not realize tenacious tracking, therefore this method can not be adapted to Gao Zhongying radar to the detection and tracking of Weak target.Gao Zhongying radar weak target detection tracking proposed by the present invention based on CS-PF is based on solution problems.The present invention can realize effective detection and tracking to Weak target while solving the measurement of Gao Zhongying distance by radar and obscuring, overcome the limitation based on the application of general PF-TBD method, the Gao Zhongyings such as airborne PD radar is adapted to the detection and tracking of Weak target, therefore there is stronger engineering application value and promotion prospect.
Description
Technical field
The present invention relates to a kind of radar data processing methods, obscure and low detection probability more particularly to a kind of range measurement
In the case of target detection tracking method, be adapted to Gao Zhongying radar to the detection and tracking of Weak target.
Background technique
Weak target largely occurs bringing stern challenge, the inspection of Weak target to traditional Radar Targets'Detection tracking
Survey the difficulties that tracking has become target detection tracking field.Therefore, effective detection of the radar to Weak target how is realized
And tracking, it is of great significance to radar fighting efficiency is improved, is the hot and difficult issue problem of current research.In order to realize to weak
Effective detection and tracking of Small object, has carried out a large amount of research both at home and abroad, and proposes based on Hough transform, Dynamic Programming
And (TBD) method is tracked before the detection such as particle filter (PF), wherein TBD (PF-TBD) method based on PF is due to algorithm letter
List is suitable for the advantages that nonlinear and non-Gaussian system, has obtained extensive concern.PF-TBD method is by carrying out metric data
Prolonged accumulation is to improve target signal to noise ratio, to realize the effective detection and tracking to Weak target.This method is mainly led to
Cross following steps realization：
(1) filter initialization obtains primary collection；
(2) one-step prediction is carried out to already present particle collection and obtains prediction particle collection；
(3) prediction particle collection particle weights are updated using new measure；
(4) dbjective state is estimated.
There are an apparent defects for Dim targets detection tracking based on PF-TBD, i.e., when radar uses Gao Zhongying
Operating mode when, to the range measurement of target there are the fuzzy of height, PF-TBD method is caused to accumulate to Weak target
There is serious accumulation mistake in process, can not realize tenacious tracking, therefore this method can not be adapted to Gao Zhongying radar to weak
The detection and tracking of Small object.
Summary of the invention
The purpose of the present invention is to propose to a kind of small and weak mesh of Gao Zhongying radar for being based on compressed sensing-particle filter (CS-PF)
Detecting and tracking method is marked, Gao Zhongying radar can not be adapted to the detection and tracking of Weak target by solving general PF-TBD method
The problem of.
The technical scheme comprises the following steps for CS-PF method proposed by the present invention：
Step 1：Initialization of variable enables k=0
(1) K is the radar switching-off moment, and S is that radar monitors region, and T is the scan period of radar, (xs,ys) be radar seat
Mark, Ru,maxFor the maximum unam of radar, zkFor the radar measurement at k moment, RkFor radar measurement error covariance, nRWith
nBThe respectively number of distance by radar and localizer unit, N=nR×nBFor the dimension of signal, R and B are respectively distance by radar and side
The size of position resolution cell, LRAnd LBLoss constant respectively on distance by radar and orientation；
(2)NpFor the population that filter uses, qkFor target occur initial distribution,It is initial point of PIN increment
Cloth, ax,maxAnd ay,maxRespectively peak acceleration of the target in the direction x and the direction y；
(3)mRAnd mBThe respectively number of range search unit and bearing search unit, M=mR×mBFor the number of atom,
Δ u and Δ v is respectively the size of distance and bearing search unit；
(4) SNR is target signal to noise ratio, and γ is target presence or absence decision threshold, and Ψ is over-complete dictionary of atoms matrix, and Φ is
Observing matrix, MgThe dimension of observation signal, vkFor random noise, ∏mFor PIN incremental transfer matrix；
(5) function round (x, y) indicates to take the maximum integer less than or equal to x/y, and function ceil (x, y) expression, which takes, to be greater than
Smallest positive integral equal to x/y, function max (x) indicate the maximum value of all elements in vector x；
Step 2：Filter initialization, to any p ∈ { 1,2 ..., Np}
(1) it samples to obtain the position of target from initial distribution qkAnd speedInformation passes through
Target pulse space-number is calculatedInformation, and enableObtain particle
(2) from initial distributionMiddle sample PI N increment
(3) particle is assignedWeight
Step 3：K=k+1 is enabled, the radar measurement at k moment is obtained
The signal that radar is received carries out A/D transformation, obtains the radar measurement at k moment Radar data is sent to handle computer；
Step 4：Construct over-complete dictionary of atoms matrix
(1) it to any m ∈ { 1,2 ..., M } and n ∈ { 1,2 ..., N }, enables
Wherein
And
(2) it enables
Obtain over-complete dictionary of atoms matrix Ψ；
(3) it to any n ∈ { 1,2 ..., N }, enables
Wherein
(4) it enablesZ will be measuredkIt is transformed into a dimensional vector sk；
Step 5：Target presence or absence judgement
(1) it enables
By skΨ is projected to, s is obtainedkProjection z on Ψatom,k；
(2) η is enabledtarget=0.9 × 2SNR/3, γ=max (1,0.75 (1+ ηtarget)), obtain target presence or absence decision gate
Limit γ；
(3) if max (zatom,k) < γ, then it is assumed that target is not present in current time, 2 is gone to step, otherwise it is assumed that current time
There are targets；
Step 6：Objective fuzzy measurement information extracts
(1) to any l ∈ 1,2 ..., MgAnd m ∈ { 1,2 ..., M }, it enables
Wherein, function randn (1) indicates to generate a random number according to standardized normal distribution；
(2) it enables
Generate observing matrix Φ；
(3) A is enabledk=Φ Ψ is made using matching pursuit algorithm solutionReach the smallest
Solution
(4) vector is foundThe serial number m of middle greatest member, and enable
And the fuzzy distance for calculating target measures ramb,kWith azimuthal measuring bk
The fuzzy object obtained under range measurement ambiguity measures zamb,k=[ramb,k bk]T；
Step 7：Particle collection prediction, to any p ∈ { 1,2 ..., Np}
(1) according to the k-1 momentWith PIN incremental transfer matrix ΠmPredict particleIncrease in the PIN at k moment
Amount
(2) basisParticleAnd dbjective state equation of transfer
It is sampled, obtains particleWherein Bk=[0 000 1]T,
And state-transition matrix and process noise control matrix are respectively
VkFor process noise, covariance is
Step 8：Particle collection weight based on blur measurement updates, to any p ∈ { 1,2 ..., Np}
(1) blur measurement of prediction is calculated
(2) new breath is calculated
(3) particle weights are updated
Step 9：Dbjective state and PIN estimation
(1) particle weights are normalized, to any p ∈ { 1,2 ..., Np}
(2) to particle collectionIt carries out resampling and obtains the particle collection at k moment
(3) to the particle collection after resamplingIt is weighted and averaged, obtains state estimation
Step 10：Step 3~step 9 is repeated, until radar switching-off.
Gao Zhongying radar weak target detection tracking proposed by the present invention based on CS-PF, can solve general
PF-TBD method can not be adapted to the problem of detection and tracking of the Gao Zhongying radar to Weak target, improve PF-TBD method
Adaptation range.
Detailed description of the invention
Attached drawing 1 is the bulk flow of the Gao Zhongying radar weak target detection tracking proposed by the present invention based on CS-PF
Cheng Tu；
Attached drawing 2 is that the effect of CS-PF method detecting and tracking Weak target in the embodiment of the present invention is shown, asterisk in attached drawing
The actual position of " * " expression target, circle "□" indicate that the range ambiguity that radar obtains measures；
Attached drawing 3 is that the effect of CS-PF method detecting and tracking Weak target in the embodiment of the present invention is shown, asterisk in attached drawing
" * " indicates that the actual position of target, circle " o " indicate the dbjective state of estimation；
Attached drawing 4 is that CS-PF method detection target exists and the comparison of real goal existence, attached drawing in the embodiment of the present invention
Middle asterisk " * " indicates that real goal existence, circle " o " indicate the target existence of detection, and " 0 " indicates that target is not deposited
In " 1 " expression target presence.
Specific embodiment
CS-PF method proposed by the present invention is described in detail with reference to the accompanying drawing.
Without loss of generality, be arranged a two-dimensional simulating scenes, radar monitor region be S=[10km, 120km] × [0,
Pi/2], radar switching-off moment K=30s, the scan period T=1s of radar, radar fix (xs,ys)=(0km, -10km), distance
Error in measurement standard deviation and azimuth error in measurement standard deviation are respectively 0.2km and 0.0087rad, radar it is most very much not fuzzy away from
From Ru,max=4.8km, distance by radar unit number nR=50, localizer unit number nB=180, the loss constant on distance by radar
LR=1, the loss constant L in orientationB=0.0082；The population N that filter usesp=3000；The initial distribution that target occurs
qkEntirely to monitor being uniformly distributed in the S of region, the initial distribution of PIN incrementWhereinPeak acceleration a of the target in the direction x and the direction yx,max=ay,max=0.01km；Range search
Unit is mR=50, the number of bearing search unit is mB=180；Target signal to noise ratio SNR=3dB, the dimension M of observation signalg=
150, PIN incremental transfer matrixes
Its step is as shown in Fig. 1.
(1) initialization of variable is carried out according to the above simulated conditions
By the above simulated conditions it is found that the dimension N=n of signalR×nB=9000, the size R=of distance by radar resolution cell
Ru,max/nR=0.096km, the size B=pi/2 n of azimuth discrimination unitB=0.0087rad, the number M=m of atomR×mB=
9000, the size delta u=R of range search unitu,max/mR=0.096km, the size delta v=π of bearing search unit/(2 ×
180)=0.0087rad, state-transition matrix, process noise control matrix, process noise covariance and error in measurement covariance
Respectively
(2) device initialization is filtered by method described in Summary step 2；
(3) radar measurement is obtained by method described in Summary step 3；
(4) by method construct over-complete dictionary of atoms matrix described in Summary step 4；
(5) target presence or absence differentiation is carried out by method described in Summary step 5；
(6) objective fuzzy measurement information is extracted by method described in Summary step 6；
(7) prediction of particle collection is carried out by method described in Summary step 7；
(8) the particle collection weight based on blur measurement is carried out by method described in Summary step 8 to update；
(9) dbjective state and PIN estimation are carried out by method described in Summary step 9；
(10) circulation executes Summary step 3~step 9, until radar switching-off.
In embodiment condition, there are range measurements to obscure for radar, and measured value cannot reflect the truth of target (see attached drawing
2), in the case where target signal to noise ratio sees SNR=3dB, CS-PF method proposed by the present invention is still able to achieve effective inspection to target
It surveys and target loss occurs in tracking, only 5 moment, target correct detection probability is greater than 0.83 (see attached drawing 3), realizes height
Repetition radar is to effective detection and tracking of Weak target, therefore the present invention overcomes the limitations of general PF-TBD method
Property.
Claims (1)
1. the Gao Zhongying radar weak target detection tracking based on CS-PF, feature include the following steps：
Step 1：Initialization of variable enables k=0
(1) K is the radar switching-off moment, and S is that radar monitors region, and T is the scan period of radar, (xs,ys) be radar coordinate,
Ru,maxFor the maximum unam of radar, zkFor the radar measurement at k moment, RkFor radar measurement error covariance, nRAnd nBPoint
Not Wei distance by radar and localizer unit number, N=nR×nBFor the dimension of signal, R and B are respectively distance by radar and orientation point
Distinguish the size of unit, LRAnd LBLoss constant respectively on distance by radar and orientation；
(2)NpFor the population that filter uses, qkFor target occur initial distribution,For the initial distribution of PIN increment,
ax,maxAnd ay,maxRespectively peak acceleration of the target in the direction x and the direction y；
(3)mRAnd mBThe respectively number of range search unit and bearing search unit, M=mR×mBFor the number of atom, Δ u and
Δ v is respectively the size of distance and bearing search unit；
(4) SNR is target signal to noise ratio, and γ is target presence or absence decision threshold, and Ψ is over-complete dictionary of atoms matrix, and Φ is observation
Matrix, MgThe dimension of observation signal, vkFor random noise, ΠmFor PIN incremental transfer matrix；
(5) function round (x, y) indicates to take the maximum integer less than or equal to x/y, and function ceil (x, y) expression, which takes, to be more than or equal to
The smallest positive integral of x/y, function max (x) indicate the maximum value of all elements in vector x；
Step 2：Filter initialization, to any p ∈ { 1,2 ..., Np}
(1) from initial distribution qkSampling obtains the position of targetAnd speedInformation passes through
Target pulse space-number is calculatedInformation, and enableObtain particle
(2) from initial distributionMiddle sample PI N increment
(3) particle is assignedWeight
Step 3：K=k+1 is enabled, the radar measurement at k moment is obtained
The signal that radar is received carries out A/D transformation, obtains the radar measurement at k moment
Radar data is sent to handle computer；
Step 4：Construct over-complete dictionary of atoms matrix
(1) it to any m ∈ { 1,2 ..., M } and n ∈ { 1,2 ..., N }, enables
Wherein
And
(2) it enables
Obtain over-complete dictionary of atoms matrix Ψ；
(3) it to any n ∈ { 1,2 ..., N }, enables
Wherein
(4) it enablesZ will be measuredkIt is transformed into a dimensional vector sk；
Step 5：Target presence or absence judgement
(1) it enables
By skΨ is projected to, s is obtainedkProjection z on Ψatom,k；
(2) η is enabledtarget=0.9 × 2SNR/3, γ=max (1,0.75 (1+ ηtarget)), obtain target presence or absence decision threshold
γ；
(3) if max (zatom,k) < γ, then it is assumed that target is not present in current time, goes to step 2, otherwise it is assumed that current time exists
Target；
Step 6：Objective fuzzy measurement information extracts
(1) to any l ∈ 1,2 ..., MgAnd m ∈ { 1,2 ..., M }, it enables
Wherein, function randn (1) indicates to generate a random number according to standardized normal distribution；
(2) it enables
Generate observing matrix Φ；
(3) A is enabledk=Φ Ψ is made using matching pursuit algorithm solutionReach the smallest solution
(4) vector is foundThe serial number m of middle greatest member, and enable
And the fuzzy distance for calculating target measures ramb,kWith azimuthal measuring bk
The fuzzy object obtained under range measurement ambiguity measures zamb,k=[ramb,k bk]T；
Step 7：Particle collection prediction, to any p ∈ { 1,2 ..., Np}
(1) according to the k-1 momentWith PIN incremental transfer matrix ΠmPredict particleIn the PIN increment at k moment
(2) basisParticleAnd dbjective state equation of transfer
It is sampled, obtains particleWherein Bk=[0 000 1]T, and
State-transition matrix and process noise control matrix are respectively
VkFor process noise, covariance is
Step 8：Particle collection weight based on blur measurement updates, to any p ∈ { 1,2 ..., Np}
(1) blur measurement of prediction is calculated
(2) new breath is calculated
(3) particle weights are updated
Step 9：Dbjective state and PIN estimation
(1) particle weights are normalized, to any p ∈ { 1,2 ..., Np}
(2) to particle collectionIt carries out resampling and obtains the particle collection at k moment
(3) to the particle collection after resamplingIt is weighted and averaged, obtains state estimation
Step 10：Step 3~step 9 is repeated, until radar switching-off.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919999A (en) * | 2019-01-31 | 2019-06-21 | 深兰科技（上海）有限公司 | A kind of method and device of target position detection |
| CN110726988A (en) * | 2019-10-30 | 2020-01-24 | 中国人民解放军海军航空大学 | Distance and speed fuzzy mutual solution method for detecting hypersonic target by PD radar |
| CN112649802A (en) * | 2020-12-01 | 2021-04-13 | 中国人民解放军海军航空大学 | Tracking method before weak and small multi-target detection of high-resolution sensor |
-
2018
- 2018-08-03 CN CN201810874680.8A patent/CN108919255A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919999A (en) * | 2019-01-31 | 2019-06-21 | 深兰科技（上海）有限公司 | A kind of method and device of target position detection |
| CN109919999B (en) * | 2019-01-31 | 2021-06-11 | 深兰科技（上海）有限公司 | Target position detection method and device |
| CN110726988A (en) * | 2019-10-30 | 2020-01-24 | 中国人民解放军海军航空大学 | Distance and speed fuzzy mutual solution method for detecting hypersonic target by PD radar |
| CN110726988B (en) * | 2019-10-30 | 2021-08-27 | 中国人民解放军海军航空大学 | Distance and speed fuzzy mutual solution method for detecting hypersonic target by PD radar |
| CN112649802A (en) * | 2020-12-01 | 2021-04-13 | 中国人民解放军海军航空大学 | Tracking method before weak and small multi-target detection of high-resolution sensor |
| CN112649802B (en) * | 2020-12-01 | 2022-03-22 | 中国人民解放军海军航空大学 | Tracking method before weak and small multi-target detection of high-resolution sensor |
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