CN109471081A - Single pulse radar weak and small target combined detection and state estimation method - Google Patents
Single pulse radar weak and small target combined detection and state estimation method Download PDFInfo
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- CN109471081A CN109471081A CN201811318238.3A CN201811318238A CN109471081A CN 109471081 A CN109471081 A CN 109471081A CN 201811318238 A CN201811318238 A CN 201811318238A CN 109471081 A CN109471081 A CN 109471081A
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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
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Abstract
The invention discloses a monopulse radar weak and small target joint detection and state estimation method, which comprises the following steps: preprocessing radar echoes; initializing filtering; predicting the state; updating the state; importance resampling; resampling movement; and (6) state estimation. The invention simultaneously obtains target detection and state estimation under the condition of low signal-to-noise ratio, and realizes accurate detection of the target and stable estimation of amplitude, Doppler and monopulse ratio; the method is easy to realize, strong in practicability and good in effect, and has wide application prospect in the field of weak and small target detection and processing of the monopulse radar.
Description
Technical field
The invention belongs to radar signal processing field, in particular to a kind of monopulse radar Weak target joint-detection and shape
State estimation method.
Background technique
The fast development of invisbile plane and cruise missile is so that carry out the demand day of detection and tracking to low signal-to-noise ratio target
It is increasingly acute.Due to limited electromagnetic capacity and antenna aperature, this demand is especially urgent for radar seeker.It is led in radar
It takes the lead search phase early stage, generally uses high repetition pulse Doppler system, it can provide for target of meeting head on and contain only thermal noise
The detection of background.It is general after Coherent processing that detection performance is promoted using no-coherence cumulating and binary integration.But radar number
The time of coherent accumulation interval, no-coherence cumulating and binary integration is limited according to the Unknown Motion of rate and target.
Pulse can be used for as a kind of angle measurement technique widely applied in tracking radar to target bearing and pitching
The precise measurement at angle, but under low signal-to-noise ratio, due to the difficulty of target detection, the estimated energy of monopulse system is poor, leads
Cause the guidance performance decline of radar seeker serious.
Summary of the invention
It is an object of the present invention in view of the above shortcomings of the prior art, provide a kind of based on poor channel particle TBD's
Monopulse radar Weak target joint-detection and method for estimating state solve target detection and angle under Low SNR
Estimation problem, the accurate detection and stablizing for amplitude, Doppler and pulse ratio for realizing target are estimated.
In order to solve the above technical problems, the technical scheme adopted by the invention is that:
A kind of monopulse radar Weak target joint-detection and method for estimating state, its main feature is that the following steps are included:
Step A. pre-processes radar return:
If the state vector of target isWhereinFor the Doppler frequency of kth frame,For
Kth frame and channel echo amplitude, γkFor the pulse ratio of kth frame target；fd=2vr/ λ, wherein vrBetween radar and target
Radial velocity, λ is carrier wavelength；Target is E there are variable in definition wave beamkAnd Ek∈ { 0,1 }, 0, which represents target, does not occur, and 1
Represent target appearance, EkFor a two condition Markov chain, state-transition matrixWherein PbFor target
Newborn probability, PdFor target extinction probability；Execute step A1~A2:
Step A1, to monopulse radar and channel reception echoWith poor channel reception echoCarry out adding window
FFT processing, after obtaining correlative accumulation and difference signal
Step A2, to the signal after correlative accumulationTake amplitudeAs observation data, then from no clutter areaMiddle selection target unit section is as observation data；
Step B. filtering initialization: step B1~B2 is executed:
Step B1, k=0 generate NsA particle, wherein NsFor particle number used in filtering；
Step B2 is right0,1 value is carried out by the probability that initial target occurs, ifBy newborn probability qb
(x0|z0) generateIfThenIt is undefined；
Step C. status predication: step C1~C2 is executed:
Step C1, according to the particle of k-1 frameState-transition matrixGenerate kth frame
ParticleIfThenIt is undefined；If Then according to xk+1=xk+vkPrediction
WhereinFor correspondenceProcess noise vector, each component is zero-mean
White Gaussian noise, vkCovariance matrix beWhereinRespectively Doppler frequency,
The variance of heave component is corresponded to channel amplitude, pulse；
Step C2, for newborn particle, according to qb(xk|zk) generate
Step D. state updates: execute step D1~D4:
Step D1, by priori probability densityAs the importance density function, using the likelihood ratio of particle as
Non- normalized weight, according to the following formula calculate and channel with difference channel conditional likelihood
WhereinByIt is calculated；Above formula needs to spread in view of target energy in calculating
To adjacent frequency cells, for the unit C (x by object effectsk)={ i0-p,...,i0-1,i0,i0+1,...,i0+p}(i0
For the nearest unit of range prediction value, p is the parameter of setting), it is distributed using the Gaussian approximation of propagation function, according to known window
Function carries out set point number Napp=quantization storage, calculateWhen, quantized value is directly read from ROM；
Step D2, byThe combination condition likelihood in calculating and channel and poor channel according to the following formula
Step D3 calculates the respective weights of particle according to the following formula:
Step D4 calculates particle normalized weight by following formula:
Step E. importance resampling: step E1~E2 is executed:
Step E1, using system resampling methods from particle collectionIt is middle to obtain new particle collection
Step E2 sets new particle as the probability of primary particle and is the corresponding weight of the particle, each grain after resampling
The normalized weight of son is 1/Ns；
Step F. resampling is mobile: execute step F1~F4:
Distribution is suggested in step F1, definitionThus suggest that distribution generates new sample, new sample to each sample
ThisAccording toIt obtains, remaining quantity of state remains unchanged；
Step F2, new particle according toIt makes decisions to receive or give up, ifThe amplitude of new particleRetain；Otherwise only work asWhen retain new particle, wherein U be 0 to 1 section
The random number being evenly distributed；
Distribution q is suggested in step F3, definitionm(γ′k|γk), thus suggest that distribution generates new sample to each sample, newly
The pulse ratio γ ' of samplekAccording to γkIt obtains, remaining quantity of state remains unchanged；
Step F4, new particle according toIt makes decisions again and receives or give up to determine, such as
Fruit Tγ′,γOtherwise U < T is only worked as in > 1, the pulse ratio γ ' reservation of new particleγ′,γWhen retain new particle；
Step G. state estimation: step G1~G2 is executed:
Step G1 estimates the posteriority existing probability of target according to the following formula
Step G2, willIt is compared with the thresholding Th of setting, ifThen determine to detect target, and leads to
Cross following formula estimated state:
Tracking (TBD) algorithm uses the data without threshold judgement before detection, or use is more compared to conventional detector
Low thresholding accumulates multiframe information according to the dynamic model of target, can provide the detection and tracking knot of target simultaneously
Fruit, to promote the detection and tracking performance of low signal-to-noise ratio target.Typical TBD implementation method has Hough transform method and dynamic
Law of planning (see " extraction of small target deteection and its motion information under sea clutter background ", naval aviation engineering college journal, the 22nd
Volume, the 1st phase, 2007 the 137-144 page), wherein dynamic programming method field of radar research and apply it is more, and
Hough transform is then mainly used for the less occasion of target maneuver (tracking of such as ballistic missile).These methods are needed while being handled
Multiframe data (batch processing) carry out discretization to state space and commonly required operand is larger.TBD based on particle filter
Method (see " application of the particle filter algorithm in TBD target detection ", research institute, China Electronics journal, volume 6, the 1st
Phase, 20011 the 91-95 pages), compared with traditional TBD method, using Recursion process, do not need storage and processing multiframe data,
Target movement model can not need to carry out discretization to state space to be non-linear.Currently, PF-TBD (particle filter TBD)
It is mainly used in search radar and over-the-horizon radar, not yet occurs applying for the PF-TBD of monopulse radar.And in pulse
In radar system and channel and poor channel be simultaneous, conventional detection estimation process flow be by and port number
According to being handled, the Doppler frequency and echo strength of target are estimated, and orientation and pitch angle then use it is traditional
Single pulse method is estimated.This processing method can not efficiently use the target letter contained in poor channel in detection-phase
Breath, in fact when target deviate beam center when, the signal amplitude in poor channel with and channel be comparable, the benefit of poor channel information
With will be helpful to improve detection performance.
The present invention is directed to high repetition pulse Doppler monopulse radar, is improved under low signal-to-noise ratio using signal processing means
Target detection and estimation performance, by using the data without threshold judgement, the dynamic model based on target completes multiframe signal
Accumulation；By fusion and poor channel information, effective integration difference channel increases search phase and target than amplitude information
The not detection available information amount at wave beam maximum direction；By the way that target numbers and corresponding dbjective state are used unified probability
Density function description, the estimation of existing probability and dbjective state is obtained by Bayes's recursive filtering；Shape is realized by using particle
Formula, while obtaining target detection and state estimation under Low SNR, realize target accurate detection and amplitude,
Doppler and stablizing for pulse ratio are estimated；The present invention is easily achieved, practical, and effect is good, in the small and weak mesh of monopulse radar
Mark detection processing field has broad application prospects.
Detailed description of the invention
Fig. 1 is the motion profile of the guided missile and target under typical scene.
Fig. 2 is flow chart of the present invention.
Fig. 3 is that the target existing probability for carrying out the different moments obtained after TBD processing to echo data under typical scene is estimated
Count result.
Fig. 4 is the amplitude Estimation result for obtain after TBD processing to echo data under typical scene.
Fig. 5 is the Doppler's estimated result for obtain after TBD processing to echo data under typical scene.
Fig. 6 is the pulse compared estimate result for obtain after TBD processing to echo data under typical scene.
Specific embodiment
Binary channels particle TBD monopulse radar Weak target of the present invention is combined with example with reference to the accompanying drawing and is examined
Estimation method is surveyed to be further elaborated.
This example is Gao Zhongying radar, wherein monopulse radar carrier frequency wavelength X=3cm, CPI (pulse repetition period)=
Umber of pulse N=5000 in 20ms, each CPI, target velocity 280m/s, Maneuver Acceleration 6g, missile velocity 1200m/s, just
Beginning Signal to Noise Ratio (SNR)=6dB.Fig. 1 gives Attack Scenarios: for guided missile along rectilinear flight, monopulse radar scanner is oriented to south by west 1
Degree, after 10 frames, target enters the main beam of radar seeker, and then target carries out 2s evasive maneuvering.
Fig. 2 gives flow chart of the invention, includes the following steps:
Step A. pre-processes radar return:
In step A1, each CPI received to the target seeker radar of guided missile and channel echoAnd poor channel is returned
WaveHamming window windowing process is carried out, FFT correlative accumulation, after being accumulated and difference signal are then carried out
Step A2, to signal after correlative accumulationTake amplitudeObservation data are constituted by no clutter area signal
Section, since no clutter area corresponding unit is longer, choose comprising target the 3100 to 3300th totally 200 units as observation data
Step B. filtering initialization:
Step B1, k=0 generate Ns=4000 particles.
Step B2 is right0,1 value is carried out by the probability that initial target occurs, ifBy newborn probability
qb(x0|z0) generateThe wherein newborn probability P of targetbWith extinction probability PdIt is 0.05, target is initially present probabilityγ0~U (0.79,0.8),For being uniformly distributed on 200 units；If
ThenIt is undefined.
Step C. status predication:
Step C1, according to the particle of k-1 frameState-transition matrixGenerate kth frame
ParticleIfThenIt is undefined；If Then according to xk+1=xk+vkPredictionIts
Middle Doppler frequency heave component and channel amplitude heave component, pulse are respectively than the variance of heave component
Step C2, for newborn particle, i.e.,According to qb(xk|zk) generate
Step D. state updates:
Step D1, by priori probability densityAs the importance density function, using the likelihood ratio of particle as
Non- normalized weight.The conditional likelihood in calculating and channel and poor channel according to the following formula
Wherein p=1, quantization points N of the propagation function in a unitapp=64.
Step D1 is calculated acquisition by step D2The connection in calculating and channel and poor channel according to the following formula
Close conditional likelihood
Step D3 calculates the respective weights of particle according to the following formula
Step D4 calculates particle normalized weight by following formula
Step E. importance resampling:
Step E1, using system resampling methods from particle collectionIt is middle to obtain new particle collection
Step E2 sets new particle as the probability of primary particle and is the corresponding weight of the particle, each grain after resampling
The normalized weight of son is 1/Ns。
Step F. resampling is mobile:
Distribution is suggested in step F1, definitionThus suggest that distribution generates new sample, new sample to each sample
ThisAccording toIt obtains, remaining quantity of state remains unchanged；Wherein AΣIt is recommended that distribution variance is 0.04.
Step F2, new particle according toIt makes decisions to receive or give up, ifThe amplitude of new particleRetain；Otherwise only work asShi Youxian retains new particle, and wherein U is 0 to 1
The random number that section is evenly distributed.
Distribution q is suggested in step F3, definitionm(γ′k|γk), thus suggest that distribution generates new sample to each sample, newly
The pulse ratio γ ' of samplekAccording to γkIt obtains, remaining quantity of state remains unchanged；Wherein the suggestion distribution variance of γ is 0.01.
Step F4, new particle according toIt makes decisions again and receives or give up to determine, such as
Fruit Tγ′,γOtherwise U < T is only worked as in > 1, the pulse ratio γ ' reservation of new particleγ′,γWhen retain new particle；U is flat for 0 to 1 section
The random number being distributed.
Step G. state estimation:
Step G1 estimates the posteriority existing probability of target according to the following formula
Obtain the corresponding target existing probability change curve of different data frame shown in Fig. 3.As it can be seen that existing and disappearing in target
Between being overdue schedule time, the present invention to the estimation of target existing probability be better than tradition merely with and channel information method.
Step G2, the target existing probability that estimation is obtainedIt is compared with the thresholding Th=0.6 of setting, ifThen determine to detect target, and dbjective state vector is estimated by following formula
It can get A under Fig. 4, Fig. 5, different data frame shown in fig. 6Σ、fd, γ estimation curve.It can be seen that when target is in
When radar beam adjacent edges (γ > 0.1), estimation performance of the invention is better than tradition merely with the method with channel information.
To sum up, the present invention is based on the TBD methods of the particle filter of monopulse radar including the use of the information of poor channel signal
Participate in analysis modeling and the entire completely new TBD detection based on particle filter in the case where existing simultaneously with poor information
The algorithm flow of method.Accumulation method and poor information fusion method, target numbers and corresponding target including multiframe signal
The probability density function of state describes method, the estimation method of existing probability and dbjective state；Low SNR is obtained simultaneously
Under target detection and state estimation method, the accurate detection of target and the stabilization of amplitude, Doppler and pulse ratio are estimated
The method of meter.
Apply the present invention to monopulse radar, and efficiently use the target information that poor channel is contained in detection-phase,
Detection performance can be improved.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than limitation, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, within these are all belonged to the scope of protection of the present invention.
Claims (1)
1. a kind of monopulse radar Weak target joint-detection and method for estimating state, which comprises the following steps:
Step A. pre-processes radar return:
If the state vector of target isWhereinFor the Doppler frequency of kth frame,For kth frame
With channel echo amplitude, γkFor the pulse ratio of kth frame target；fd=2vr/ λ, wherein vrRadial direction between radar and target
Speed, λ are carrier wavelength；Target is E there are variable in definition wave beamkAnd Ek∈ { 0,1 }, 0, which represents target, does not occur, and 1 represents mesh
Mark existing, EkFor a two condition Markov chain, state-transition matrixWherein PbFor target new life
Probability, PdFor target extinction probability；Execute step A1~A2:
Step A1, to monopulse radar and channel reception echoWith poor channel reception echoIt carries out at windowing FFT
Reason, after obtaining correlative accumulation and difference signal
Step A2, to the signal after correlative accumulationTake amplitudeAs observation data, then from no clutter areaMiddle selection target unit section is as observation data；
Step B. filtering initialization: step B1~B2 is executed:
Step B1, k=0 generate NsA particle, wherein NsFor particle number used in filtering；
Step B2 is right0,1 value is carried out by the probability that initial target occurs, ifBy newborn probability qb(x0|
z0) generateIfThenIt is undefined；
Step C. status predication: step C1~C2 is executed:
Step C1, according to the particle of k-1 frameState-transition matrixGenerate the particle of kth frameIfThenIt is undefined；If Then according to xk+1=xk+vkPredictionWhereinFor correspondenceProcess noise vector, each component is the Gauss of zero-mean
White noise, vkCovariance matrix beWhereinRespectively Doppler frequency and
Channel amplitude, pulse correspond to the variance of heave component；
Step C2, for newborn particle, according to qb(xk|zk) generate
Step D. state updates: execute step D1~D4:
Step D1, by priori probability densityAs the importance density function, using the likelihood ratio of particle as not returning
One changes weight, according to the following formula the conditional likelihood in calculating and channel and poor channel
WhereinByIt is calculated；
Step D2, byThe combination condition likelihood in calculating and channel and poor channel according to the following formula
Step D3 calculates the respective weights of particle according to the following formula:
Step D4 calculates particle normalized weight by following formula:
Step E. importance resampling: step E1~E2 is executed:
Step E1, using system resampling methods from particle collectionIt is middle to obtain new particle collection
Step E2 sets new particle as the probability of primary particle and is the corresponding weight of the particle, each particle after resampling
Normalized weight is 1/Ns；
Step F. resampling is mobile: execute step F1~F4:
Distribution is suggested in step F1, definitionThus suggest that distribution generates new sample to each sample, new samplesAccording toIt obtains, remaining quantity of state remains unchanged；
Step F2, new particle according toIt makes decisions to receive or give up, ifThe amplitude of new particleRetain；Otherwise only work asWhen retain new particle, wherein U be 0 to 1 section
The random number being evenly distributed；
Distribution q is suggested in step F3, definitionm(γ′k|γk), thus suggest that distribution generates new sample, new samples to each sample
Pulse ratio γ 'kAccording to γkIt obtains, remaining quantity of state remains unchanged；
Step F4, new particle according toIt makes decisions again and receives or give up to determine, if
Tγ′,γOtherwise U < T is only worked as in > 1, the pulse ratio γ ' reservation of new particleγ′,γWhen retain new particle；
Step G. state estimation: step G1~G2 is executed:
Step G1 estimates the posteriority existing probability of target according to the following formula
Step G2, willIt is compared with the thresholding Th of setting, ifThen determine to detect target, and under passing through
Formula estimated state:
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109991597A (en) * | 2019-04-04 | 2019-07-09 | 中国人民解放军国防科技大学 | Weak-expansion-target-oriented tracking-before-detection method |
| CN110097569A (en) * | 2019-04-04 | 2019-08-06 | 北京航空航天大学 | Oil tank object detection method based on color Markov Chain conspicuousness model |
| CN111352104A (en) * | 2020-03-18 | 2020-06-30 | 清华大学 | Weak target tracking-before-detection method based on information accumulation |
| CN111398928A (en) * | 2020-05-08 | 2020-07-10 | 北京理工大学重庆创新中心 | Method for calculating detection threshold of synthetic ultra-narrow pulse radar based on resampling algorithm |
| CN113126086A (en) * | 2020-12-30 | 2021-07-16 | 西安电子科技大学 | Life detection radar weak target detection method based on state prediction accumulation |
| CN113687340A (en) * | 2021-08-24 | 2021-11-23 | 重庆交通大学 | Millimeter wave radar-based remote moving target detection method |
| CN114428228A (en) * | 2022-01-24 | 2022-05-03 | 西安电子科技大学 | Clutter suppression method for high repetition frequency sum-difference antenna radar seeker |
| CN115407299A (en) * | 2022-09-15 | 2022-11-29 | 中国人民解放军国防科技大学 | Weak and small target detection method and device based on Bernoulli filter algorithm |
| CN115436902A (en) * | 2022-09-15 | 2022-12-06 | 中国人民解放军国防科技大学 | Three-channel joint detection-based angular error estimation method and device |
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| CN109991597A (en) * | 2019-04-04 | 2019-07-09 | 中国人民解放军国防科技大学 | Weak-expansion-target-oriented tracking-before-detection method |
| CN110097569A (en) * | 2019-04-04 | 2019-08-06 | 北京航空航天大学 | Oil tank object detection method based on color Markov Chain conspicuousness model |
| CN110097569B (en) * | 2019-04-04 | 2020-12-22 | 北京航空航天大学 | Oil tank target detection method based on color Markov chain significance model |
| CN111352104A (en) * | 2020-03-18 | 2020-06-30 | 清华大学 | Weak target tracking-before-detection method based on information accumulation |
| CN111398928A (en) * | 2020-05-08 | 2020-07-10 | 北京理工大学重庆创新中心 | Method for calculating detection threshold of synthetic ultra-narrow pulse radar based on resampling algorithm |
| CN113126086A (en) * | 2020-12-30 | 2021-07-16 | 西安电子科技大学 | Life detection radar weak target detection method based on state prediction accumulation |
| CN113687340A (en) * | 2021-08-24 | 2021-11-23 | 重庆交通大学 | Millimeter wave radar-based remote moving target detection method |
| CN113687340B (en) * | 2021-08-24 | 2024-02-23 | 重庆交通大学 | Long-distance moving target detection method based on millimeter wave radar |
| CN114428228A (en) * | 2022-01-24 | 2022-05-03 | 西安电子科技大学 | Clutter suppression method for high repetition frequency sum-difference antenna radar seeker |
| CN114428228B (en) * | 2022-01-24 | 2024-06-07 | 西安电子科技大学 | Clutter suppression method for high-repetition-frequency sum-difference antenna radar seeker |
| CN115407299A (en) * | 2022-09-15 | 2022-11-29 | 中国人民解放军国防科技大学 | Weak and small target detection method and device based on Bernoulli filter algorithm |
| CN115436902A (en) * | 2022-09-15 | 2022-12-06 | 中国人民解放军国防科技大学 | Three-channel joint detection-based angular error estimation method and device |
| CN115436902B (en) * | 2022-09-15 | 2024-06-14 | 中国人民解放军国防科技大学 | Angle error estimation method and device based on three-channel joint detection |
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