CN106569196A - Ground-based radar multi-target detection method based on compressed sensing - Google Patents
Ground-based radar multi-target detection method based on compressed sensing Download PDFInfo
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- CN106569196A CN106569196A CN201610984778.XA CN201610984778A CN106569196A CN 106569196 A CN106569196 A CN 106569196A CN 201610984778 A CN201610984778 A CN 201610984778A CN 106569196 A CN106569196 A CN 106569196A
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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 ground-based radar multi-target detection method based on compressed sensing. The method includes the steps that sparse representations of echo signals are completed, and a sparse transform matrix is schemed out; a random Gauss matrix is chosen as an observation matrix, and each row vector in the observation matrix is used for projecting target echo signals; part of signal information is obtained, and the echo signals undergo reduced-order observation; the sparse representations of the target echo signals are set as optimization variables, and a convex optimization model is established and solved to obtain a global optimum solution of the sparse representation signals; according to the sparse matrix construction method and the sparse representations of the echo signals, a target time-lag and a target Doppler frequency are obtained. Compared with a traditional orthogonal matching method, the method can correctly detect a large number of targets when the number of targets is uncertain.
Description
Technical field
The invention belongs to radar signal processing field, particularly a kind of ground radar multi-target detection based on compressed sensing
Method.
Background technology
In ground radar target detection, the bigger environment clutter of intensity, useful signal are often mingled with echo-signal
Flood wherein.Carrying out the measure of clutter recognition has a lot, can distinguish on antenna, transmitter, receiver and signal processor
Corresponding technological means are taken to carry out clutter reduction.Wherein, the clutter suppression method related to signal processing mainly includes：Moving-target
Show (Moving Target Indication, MTI) technology, moving-target detection (Moving Target Detection,
MTD) technology, pulse Doppler (Pulse Doppler, PD) technology etc..The 1950's, Emerson et al. propose dynamic
Target shows the concept of (Moving Target Indication, MTI), and carries out clutter recognition with this.The MTI filtering of early stage
Device realizes that effect is poor by way of analog circuit and delay line.With the development of electronic technology and Radar Signal Processing,
The mti filter of various excellent performances occurs in succession, adaptive MTI (Adaptive MTI, AMTI) wave filter[38]Can basis
The difference of clutter mid frequency changes wave filter notch position automatically, and clutter recognition performance is further improved.But pass through
After MTI clutter cancellations, still comprising residual clutter composition in signal.When noise intensity is very big, clutter residue can still disturb mesh
Target correctly detects, causes false-alarm probability to rise.To ensure there is fixed false-alarm probability, CFAR during target detection
(Constant False Alarm Rate, CFAR) is detected[39]Technology is arisen at the historic moment, and it is permanent that CFAR detection is generally divided into unit
False-alarm and time domain CFAR.It is steady in time domain using clutter amplitude in order to reduce the remaining impact to target detection of clutter
Property, time integral is carried out on azran unit, the remaining amplitude Estimation of clutter is obtained, drawn on the basis of clutter remnants
Thresholding is used for the output of target, thus reduces the false-alarm probability of target detection, here it is clutter map (Clutter Map, CM) inspection
Survey technology[40-41].20 century 70s, Massachusetts Institute Technology's Lincoln laboratory proposes moving-target detection (Moving
Target Detection, MTD) method carries out clutter recognition[42].Then occur the MTD wave filter of various different structures again with
And adaptive M TD (Adaptive MTD, AMTD) wave filter.
Compressive sensing theory (Compressed Sensing, CS) can make full use of the openness of echo measurement signal or
Compressibility, using the sampling rate far below twice signal frequency the sampling and reconstruct to signal is realized, how breaches tradition
The restriction of Qwest's sampling thheorem.By the way that compressive sensing theory is incorporated in Radar Signal Processing, echo can be substantially reduced
The sample rate of signal and data processing.
But, a kind of method that compressive sensing theory is applied to into ground radar multi-target detection is there is no in prior art.
The content of the invention
It is an object of the invention to provide a kind of ground radar multi-target detection method based on compressed sensing.The method will
Compressive sensing theory uses ground radar target detection, using the redundancy of data, only gathers a small amount of sample reduction original number
According to.Restructing algorithm adopts convex optimized algorithm, can realize the detection simultaneously to multiple targets.
The technical solution for realizing the object of the invention is：A kind of ground radar multi-target detection side based on compressed sensing
Method, including：
Step 1, rarefaction representation is carried out to echo-signal, specifically echo-signal is carried out using sparse transformation matrix sparse
Represent；
Step 2, select random Gaussian matrix as observing matrix, using each row vector in observing matrix respectively to mesh
Mark echo-signal is projected, and obtains the partial information of signal, and to echo-signal dimensionality reduction observation is carried out；
Step 3, with the rarefaction representation of target echo signal as optimized variable, set up convex Optimized model and solve, reconstruct dilute
Relieving the exterior syndrome shows the globally optimal solution of signal；
Step 4, obtained by the globally optimal solution of rarefaction representation signal in the sparse matrix and step 3 built in step 1
The time delay and Doppler frequency deviation of each target.
The present invention compared with prior art, with advantages below：1) compressive sensing theory is applied to ground thunder by the present invention
The target detection for reaching, compared with conventional target is detected, far below the speed collecting sample of nyquist sampling rate, to reduce collection
The memory space and amount of calculation of signal；2) present invention adopts convex optimized algorithm to the reconstruct of target echo signal, with it is traditional just
Hand over matching algorithm to compare, can detect between multiple targets, and each target simultaneously in the case of target numbers are uncertain
Influence each other less, therefore adaptability of the present invention is higher；3) present invention in sparse transformation matrix construction, be by detection zone draw
It is divided into delay-Doppler domain grid, the target echo in each grid is fixed, it is not necessary to do matched filtering etc. to echo-signal
Reason, directly can obtain the time delay and Doppler frequency deviation of target echo signal by reconstruction result, reduce amount of calculation, improve measurement
Precision.
The present invention is described in further detail below in conjunction with the accompanying drawings.
Description of the drawings
Fig. 1 is the inventive method flow chart.
Fig. 2 is the time shift-Doppler's stress and strain model figure in the present invention.
Fig. 3 is the echo-signal rarefaction representation result figure obtained in the embodiment of the present invention.
Fig. 4 is the theoretical echo-signal of the embodiment of the present invention and reconstruct echo-signal comparative result figure.
Specific embodiment
With reference to Fig. 1, a kind of ground radar multi-target detection method based on compressed sensing of the present invention, including following step
Suddenly：
Step 1, rarefaction representation is carried out to echo-signal, specifically echo-signal is carried out using sparse transformation matrix sparse
Represent；Specially：
Step 1-1, echo-signal x for determining M echo signal, formula used is：
In formula, τmAnd υmThe time shift of respectively m-th target and Doppler frequency deviation,Returning for m-th target
Ripple signal；
Step 1-2, delay-Doppler domain is divided into into Nw=Nτ×NυIndividual grid, temporal resolution is Δ τ, and Doppler divides
Resolution is Δ υ, so that it is determined that sparse transformation matrix, completes rarefaction representation of the target echo signal on sparse transformation matrix, institute
It is with formula：
In formula,For the corresponding target echo signal of target (i, j), now time shift is τi=(i-1) × Δ τ,
Doppler frequency deviation is υj=(j-1) × Δ υ；
Step 1-3, the coefficient representation for determining target echo signal, formula used is：
X=Psi*xp
In formula, xpIt is the N after the rarefaction representation by echo-signalw× 1 dimension matrix, and
Then xpIn only M nonzero value and M ＜ ＜ Nw- M, so xpIt is sparse.
Step 2, select random Gaussian matrix as observing matrix, using each row vector in observing matrix respectively to mesh
Mark echo-signal is projected, and obtains the partial information of signal, and to echo-signal dimensionality reduction observation is carried out；Select random Gaussian matrix
Used as observing matrix Phi, formula used is：
Phi=sqrt (1/Mwid) * randn (Mwid, Nwid)
In formula, Nwid represents the length that target echo signal is sampled, and Mwid represents the number of observation；Echo-signal is entered
Row dimensionality reduction is observed, and formula used is y=Phi*x, and y is observation, if the Mwid for choosing meets Mwid >=c × M × lg (Nwid/
M), then primary signal can be reconstructed with high probability from observation y, can not otherwise reconstructs primary signal, wherein c is constant.
Step 3, with the rarefaction representation of target echo signal as optimized variable, set up convex Optimized model and solve, reconstruct dilute
Relieving the exterior syndrome shows the globally optimal solution of signal；The convex Optimized model is：
min imize||xp||1
Subject to y=Phi*Psi*xp。
Step 4, obtained by the globally optimal solution of rarefaction representation signal in the sparse matrix and step 3 built in step 1
The time delay and Doppler frequency deviation of each target.Specifically：According to the sparse matrix Psi and globally optimal solution x of rarefaction representation signalpIn
The position that absolute value is not zero obtains the time shift of each target and Doppler frequency deviation.
Compressive sensing theory is applied to the present invention target detection of ground radar, compared with conventional target is detected, with remote
Less than the speed collecting sample of nyquist sampling rate, the memory space and amount of calculation of collection signal are reduced.
It is described in more detail below.
With reference to Fig. 1, a kind of ground radar multi-target detection method based on compressed sensing, comprise the following steps：
Step 1, rarefaction representation is carried out to echo-signal, specifically echo-signal is carried out using sparse transformation matrix sparse
Represent；With reference to Fig. 1 and 2, the step determines first echo-signal x of M echo signal, and formula used is：
In formula, τmAnd υmThe time shift of respectively m-th target and Doppler frequency deviation,Returning for m-th target
Ripple signal, is Nwid × 1 dimension matrix.
It is N that detection zone is pressed into time delay-Doppler domain stress and strain modelw=Nτ×NυIndividual grid, the interior point target of grid (i, j)
Corresponding time shift is τi=(i-1) × Δ τ, Doppler frequency deviation is υj=(j-1) × Δ υ, echo-signal isAfterwards,
Sparse transformation matrix Psi is obtained, formula used is：
Determine the coefficient representation of target echo signal, formula used is：
X=Psi*xp
In formula, xpIt is the N after the rarefaction representation by echo-signalw× 1 dimension matrix, and
From above formula, xpIn only M nonzero value and M ＜ ＜ Nw- M, so xpIt is sparse, and xpDilution factor is M.
Step 2, select random Gaussian matrix as observing matrix, using each row vector in observing matrix respectively to mesh
Mark echo-signal is projected, and obtains the partial information of signal, and to echo-signal dimensionality reduction observation is carried out；With reference to Fig. 1, the step is true
Determine observation matrix Phi and tie up random Gaussian matrix for Mwid × Nwid, formula used is：
Phi=sqrt (1/Mwid) * randn (Mwid, Nwid)
In formula, Nwid is the length of target echo signal sampling, and Mwid represents the number of observation.Echo-signal is carried out
Dimensionality reduction is observed, and formula used is：
Y=Phi*x
In formula, y is observation, and dimension is Mwid × 1 matrix.According to compressive sensing theory, if the Mwid for choosing meets
Mwid >=c × M × lg (Nwid/M) (c is constant), then can accurately reconstruct primary signal with high probability from observed value y.
Step 3, with the rarefaction representation of target echo signal as optimized variable, set up convex Optimized model and solve, reconstruct dilute
Relieving the exterior syndrome shows the globally optimal solution of signal；The step is set up convex Optimized model and is solved, and obtains the globally optimal solution of convex Optimized model.
Obtain rarefaction representation signal xp0 norm (number of non-zero value) it is minimum, but 0 norm optimization problem is to be difficult to solve, true
Prove, solve 1 norm also can approach and effect as above, so with xp1 norm be optimized variable, set up convex optimization
Model：
min imize||xp||1
Subject to y=Phi*Psi*xp
Obtain xpGlobally optimal solution, can according to delay-Doppler domain grid obtain multiobject time delay and Doppler frequency
Partially.
Step 4, obtained by the globally optimal solution of rarefaction representation signal in the sparse matrix and step 3 built in step 1
The time delay and Doppler frequency deviation of each target.
Further detailed description is done to the present invention with reference to embodiment.
Embodiment
The present invention is verified using following parameter：
Whole system composition such as Fig. 1, system is ground radar target detection, and detecting distance is 10km～12km, detects speed
Spend for 0～60m/s.Range resolution ratio is 40m, and velocity resolution is 1m/s, Nτ=40, Nυ=40.Three targets, distance are set
For [10100,11000,11600], speed is [10,20,30].
First multiobject echo-signal x is obtained according to step 1, design sparse transformation matrix Psi so that x is sparse
It is sparse under transformation matrix Psi.According to step 2, choose Mwid=500 and determine 500 × 1067 dimension random Gaussian matrixes, obtain
Length is 500 observations of 1067 target echo signal.According to step 3 with xp1 norm be optimized variable, set up convex
Optimized model：
min imize||xp||1
Subject to y=Phi*Psi*xp
Obtain the x that length is 2400pIt is non-zero in the 130th, 1220,1,950 three point value, correspondence delay-Doppler domain grid
On (3,11), (21,21), (33,31), the position and speed of corresponding three targets is respectively [10100m, 10m/s],
[11000m, 20m/s], [11600m, 30m/s], object detection results are correct.
Fig. 3 is sparse signal representation form x in examplepOutput result, Fig. 4 be original object echo-signal with reconstruct
Target echo signal comparison diagram.What as seen from the figure three targets can will be apparent that detects, and the multiple target for reconstructing is returned
Ripple signal results are correct.
From the foregoing, it will be observed that the ground radar multi-target detection method that the present invention is provided is obtained with the network analysis of delay-Doppler domain
To sparse transformation matrix, the time delay and Doppler frequency deviation of target echo signal can be directly obtained based on convex optimal reconfiguration algorithm,
Without carrying out the normal radar signal processing such as pulse compression, Sidelobe Suppression, moving-target detection to echo-signal, can effectively drop
Low amount of calculation, reduction leakage error, raising certainty of measurement.The reconstruct of echo signal adopts convex optimization method, with conventional orthogonal
Compare with method, multiple targets can be correctly detected in the case of unknown object number.
Claims (5)
1. a kind of ground radar multi-target detection method based on compressed sensing, it is characterised in that comprise the following steps：
Step 1, rarefaction representation is carried out to echo-signal, specifically sparse table is carried out to echo-signal using sparse transformation matrix
Show；
Step 2, selection random Gaussian matrix are returned respectively using each row vector in observing matrix as observing matrix to target
Ripple signal is projected, and obtains the partial information of signal, and to echo-signal dimensionality reduction observation is carried out；
Step 3, with the rarefaction representation of target echo signal as optimized variable, set up convex Optimized model and solve, reconstruct sparse table
Show the globally optimal solution of signal；
Step 4, each mesh is obtained by the globally optimal solution of rarefaction representation signal in the sparse matrix and step 3 built in step 1
Target time delay and Doppler frequency deviation.
2. the ground radar multi-target detection method based on compressed sensing according to claim 1, it is characterised in that step
Rarefaction representation is carried out in 1 to echo-signal using sparse transformation matrix to be specially：
Step 1-1, echo-signal x for determining M echo signal, formula used is：
In formula, τmAnd υmThe time shift of respectively m-th target and Doppler frequency deviation,For the echo letter of m-th target
Number；
Step 1-2, delay-Doppler domain is divided into into Nw=Nτ×NυIndividual grid, temporal resolution be Δ τ, DOPPLER RESOLUTION
For Δ υ, so that it is determined that sparse transformation matrix, completes rarefaction representation of the target echo signal on sparse transformation matrix, public affairs used
Formula is：
In formula,For the corresponding target echo signal of target (i, j), now time shift is τi=(i-1) × Δ τ, Doppler
Frequency deviation is υj=(j-1) × Δ υ；
Step 1-3, the coefficient representation for determining target echo signal, formula used is：
X=Psi*xp
In formula, xpIt is the N after the rarefaction representation by echo-signalw× 1 dimension matrix, and
Then xpIn only M nonzero value and M ＜ ＜ Nw- M, so xpIt is sparse.
3. the ground radar multi-target detection method based on compressed sensing according to claim 1, it is characterised in that step
Select random Gaussian matrix as observing matrix Phi in 2, formula used is：
Phi=sqrt (1/Mwid) * randn (Mwid, Nwid)
In formula, Nwid represents the length that target echo signal is sampled, and Mwid represents the number of observation；Echo-signal is dropped
Dimension observation, formula used is y=Phi*x, and y is observation, if the Mwid for choosing meets Mwid >=c × M × lg (Nwid/M),
Primary signal can be reconstructed with high probability from observation y, can not otherwise reconstruct primary signal, wherein c is constant.
4. the ground radar multi-target detection method based on compressed sensing according to claim 1, it is characterised in that step
Convex Optimized model is described in 3：
minimize||xp||1
Subject to y=Phi*Psi*xp。
5. the ground radar multi-target detection method based on compressed sensing according to claim 1, it is characterised in that step
4 by step 1 build sparse matrix and step 3 in rarefaction representation signal globally optimal solution obtain each target time delay and
Doppler frequency deviation, specifically：According to the sparse matrix Psi and globally optimal solution x of rarefaction representation signalpWhat middle absolute value was not zero
Position obtains the time shift of each target and Doppler frequency deviation.
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Application publication date: 20170419 Assignee: Shanghai Sogo Communication Technology Co., Ltd. Assignor: NANJING University OF SCIENCE AND TECHNOLOGY Contract record no.: X2022980011795 Denomination of invention: A ground-based radar multi-target detection method based on compressed sensing Granted publication date: 20190507 License type: Common License Record date: 20220802 |
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