CN108761412A - Compressed sensing radar single goal method for parameter estimation in the case of a kind of low signal-to-noise ratio - Google Patents
Compressed sensing radar single goal method for parameter estimation in the case of a kind of low signal-to-noise ratio Download PDFInfo
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- CN108761412A CN108761412A CN201810394558.0A CN201810394558A CN108761412A CN 108761412 A CN108761412 A CN 108761412A CN 201810394558 A CN201810394558 A CN 201810394558A CN 108761412 A CN108761412 A CN 108761412A
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- G—PHYSICS
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- 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 compressed sensing radar single goal method for parameter estimation in the case of a kind of low signal-to-noise ratio, including establish the joint sparse expression model, construction pulse compression accumulation observing matrix and joint sparse optimal reconfiguration of echo pulse signal group；Wherein：The joint sparse for establishing echo-signal group indicates that model is used to obtain the joint sparse expression of compressed sensing radar echo pulse ensemble；Construction pulse compression accumulation observing matrix is used to carry out compression accumulation to return pulse signal group, improves and receives ensemble signal-to-noise ratio；Joint sparse optimal reconfiguration is used to reconstruct echo pulse signal group in the joint sparse vector in sparse domain, obtains the estimation of target component.
Description
Technical field
The present invention relates to compressed sensing radar single goal method for parameter estimation under a kind of low signal-to-noise ratio, belong to the communications field.
Background technology
There are very noisy, compressive sensing theory will fall rapidly upon the performance of the sparse reconstruct of signal.However
Radar system is often faced with the relatively low situation of signal-to-noise ratio, and very noisy becomes compressed sensing radar and answered in hardware realization and engineering
With a great problem faced during research.In existing work, scholars surround the compressed sensing thunder under strong noise background
Superiorization restructing algorithm expands research, however when signal-to-noise ratio reduces to a certain extent, the performance of these optimal reconfiguration algorithms
Drastically decline, compressed sensing can not accurately reconstruct target scene.Therefore, it is necessary to for the pressure in the case of low signal-to-noise ratio
Contracting perception radar provides a kind of target component method of estimation based on pulse accumulation observing matrix, improves the mesh under strong noise background
Mark parameter Estimation accuracy rate.
Invention content
Goal of the invention：It is an object of the invention to solve compressed sensing radar in the case of how improving low signal-to-noise ratio to be directed to list
The problem of target component estimation performance of target.
Technical solution：To solve the above-mentioned problems, the technical solution adopted by the present invention is as follows：
Compressed sensing radar single goal method for parameter estimation in the case of a kind of low signal-to-noise ratio, including establish echo pulse signal
The joint sparse of group indicates model, construction pulse compression accumulation observing matrix and joint sparse optimal reconfiguration；Wherein：It establishes back
The joint sparse of wave ensemble indicates that model is used to obtain the joint sparse expression of compressed sensing radar echo pulse ensemble；Structure
It makes pulse compression accumulation observing matrix to be used to carry out compression accumulation to return pulse signal group, improves and receive ensemble signal-to-noise ratio；
Joint sparse optimal reconfiguration is used to reconstruct echo pulse signal group in the joint sparse vector in sparse domain, obtains estimating for target component
Meter.
Preferably, the construction process of compressed sensing radar echo pulse compression accumulation observing matrix is as follows：1) input compression
Sampled echo signals；2) rough estimate target velocity obtains velocity estimation valueGoal-selling speed interval is grouped with pulse simultaneously
Correspondence；3) according to speed interval where target, phase compensating factor is arranged with the average speed in the section；4) output point
Group pulse accumulates observing matrix Φpulse。
Preferably, the process for the correspondence that goal-selling speed interval is grouped with pulse is as follows：1) compressed sensing is calculated
The speed tolerant of radar, i.e. target rest on the maximum speed in the same range cell within the time of P pulse persistanceWherein TrIndicate the pulse duration of compressed sensing radar system, dmIndicate Range resolution unit
Size, floor () are downward bracket function；2) calculating speed estimated valueUmber of pulse h in corresponding pulse grouping,
Preferably, speed interval where being based on target, phase compensating factor, which is arranged, with the average speed in the section is specially：
The base vector of pulse accumulation observing matrix is constituted using phase compensating factorWhereinThe corresponding Doppler frequency shift amount of average speed of speed interval where target.
Preferably, pulse accumulation observing matrix ΦpulseSpecially Φpulse=diag (β1uT,β2uT,...,βQU), wherein Q
For pulse group number, i.e., the grouping number that echo-signal group obtains according to the prior information of target velocity, and Q=floor (P/
H), β1,β2,...,βQRespectively pulse group 1 is to the corresponding preferred coefficients of Q；
Preferably, compression accumulation observation signal Y is expressed as
Y=ΦdataRΦpulse
Wherein, ΦdataObserving matrix is compressed for data dimension, dimension is Z ' × Z, and Z is the length of original sampling data, and Z ' is
Compressed data length, the i.e. compression ratio of data dimension are Z '/Z；
Two-dimensional observation problem in above formula can be equivalent to following one-dimensional observation problem
Wherein y=vec (Y), r=vec (R), vec () are vectorization function, i.e., pull into two-dimensional matrix by row one-dimensional
Column vector,For Kronecker product.Due to the reception signal r of i-th of pulseiWith following sparse representation model
ri=Ψ θi
Wherein Ψ is the sparse excessively complete dictionary of compressed sensing radar system, θiIt is received corresponding to signal for i-th of pulse
Sparse vector.Enable joint sparse vector theta=[θ1,θ2,...,θP] and its vectorization after data vectorThen
It can obtain
Wherein IPIndicate the unit matrix of a P × P.
Preferably, the joint sparse restructuring procedure of data is as follows after observing pulse accumulation：1) it is J by Γ points of perception matrix
=L × M submatrix, wherein L, M are respectively the range cell number and doppler cells number that target information space is included.Definition
Recognition function derives the building method of each submatrixWherein ΓiTo perceive the i-th row of matrix Γ,For B
The A of a submatrix G is arranged, and there are following correspondence A=floor (i/J)+1, B=mod (i/J)-between A, B and i
floor((A-1)/h)；2) residual error is initializedΞ0=0.；3) it calculates
4) update Λ=Λ ∪ λ, Ξt=[Ξt-1,G(j)]；5) least square method is utilized to calculate6) residual error is updated7) judge, if cycle-index meets t >=K, terminate cycle.Otherwise it gos to step 2)
It continues cycling through.
Advantageous effect：The present invention is compared with prior art：The present invention considers observing matrix in compressed sensing radar system
To the compression function of pulse, designed based on structuring observing matrix, for compressed sensing radar system mesh in the case of low signal-to-noise ratio
The undesirable problem of reconstruction property is marked, to improve the target acquisition performance of compressed sensing radar in the case of low signal-to-noise ratio, from observation
The angle of matrix design is set out, and is provided a kind of compressed sensing radar echo pulse accumulation method based on observing matrix, is being pressed
While contracting echo data, the grouping accumulation of long echo pulse is realized, is effectively avoided while improving signal-to-noise ratio
The problem of target is across Range cell migration.
Description of the drawings
Fig. 1 is the compressed sensing radar echo pulse grouping accumulation flow chart of the present invention；
Fig. 2 is the pulse accumulation observing matrix design cycle of the present invention；
Fig. 3 is the compressed sensing radar pulse accumulation schematic diagram data of binding signal compression sampling of the present invention.
Specific implementation mode
The present invention considers the case where target is across Range cell migration, has studied a kind of confrontation target across Range cell migration
Compressed sensing radar pulse grouping accumulation observation procedure realizes compressed sensing radar by designing the observing matrix of specific structure
The grouping of echo impulse accumulates, the detection performance of compressed sensing radar in the case of significant raising low signal-to-noise ratio.
Present invention generally comprises three parts：Compressed sensing radar echo signal group group technology based on target velocity,
After compressed sensing radar pulse based on pulse grouping accumulation accumulates observing matrix design method and is observed for pulse accumulation
The combined optimization restructing algorithm of data designs.
Assuming that the receives echo-signal group R=[r of pulse regime compressed sensing radar system1,r2,…,rP] returned comprising P
Wave impulse, wherein rpFor p-th of echo impulse.Fig. 1 describes the process that compressed sensing radar echo signal group is grouped accumulation, is
Influence of the target across Range cell migration to pulse accumulation performance is avoided, the echo-signal group received is according to target velocity
Prior information (information, estimate information etc.) is divided into Q groups, i.e. q groups correspond to q-th of range cell that target is moved to,
H echo impulse in each group takes approximate coherent accumulation.Design detailed process such as Fig. 2 of pulse accumulation observing matrix
It is shown.
Before designing pulse accumulation observing matrix, it is necessary first to the correspondence of pre-set velocity interval and pulse grouping,
Umber of pulse in each pulse group can be calculated according to following step, provide the definition of speed tolerant, speed first
Tolerance vPIt represents and target is made to rest on the maximum speed in the same range cell within the time of P pulse persistance, enable TrTable
Show the pulse duration of compressed sensing radar system, dmIndicate the size of Range resolution unit, then compressed sensing radar system
Speed tolerant can be expressed as
Wherein floor (x) is downward bracket function, for acquiring the max-int for being not more than x.It is being determined by calculation
The speed tolerant v of compressed sensing radar systemPAfterwards, velocity estimation valueUmber of pulse h in corresponding pulse grouping can be expressed as
The umber of pulse in the grouping of the pulse corresponding to possible target velocity section is calculated by formula (1) and (2).True
Speed interval where setting the goal constitutes the base of pulse accumulation observing matrix using phase compensating factor with after pulse grouping situation
Vector
WhereinThe corresponding Doppler frequency shift amount of average speed of speed interval where target.
Then pulse accumulation observing matrix ΦpulseIt can be expressed as
Φpulse=diag (β1uT,β2uT,...,βQu) (4)
Wherein β1,β2,...,βQThe referred to as preferred coefficient of pulse group, the selection for optimizing pulse group, in compressed sensing radar
In long burst cumulative process, when there is the problems such as interference in short-term, target change acceleration, then can in light of the circumstances it be arranged not
The same preferred coefficient of pulse group becomes the influence of acceleration problem to reduce interference in short-term with target within a certain period of time.In order to verify
Compressed sensing radar pulse accumulates the validity of observing matrix, and echo impulse accumulation problem ideally is considered in this chapter,
Therefore in this chapter, the preferred coefficient of pulse group is 1.It is hereby achieved that the reception signal after being grouped pulse accumulation is
R '=R Φpulse (5)
The original intention of compressed sensing radar is amount of compressed data, simplifies system, is ensured preferably by minimum data volume
Target acquisition performance.Therefore, rely on compressed sensing radar echo signal compression sampling module, echo impulse group is in data dimension
It can be compressed, define ΦdataObserving matrix is compressed for data dimension, the pulse accumulation problem of such compressed sensing radar can
To be extended to the two dimensional compaction problem of the echo data block such as Fig. 3.Wherein data dimension compression observing matrix ΦdataDimension be Z '
× Z, Z are the length of original sampling data, and Z ' is compressed data length, i.e., the compression ratio of data dimension is Z '/Z.For convenience
For the sake of, the data used in this section are compression calculation matrix ΦdataUsing gaussian random observing matrix.
As can be seen from Figure 3 compression accumulation observation signal Y is the two dimensional compaction signal of primary reception multiple-pulse data R,
And it can be expressed as
Y=ΦdataRΦpulse (6)
Two-dimensional observation problem in formula (6) can be equivalent to following one-dimensional observation problem
Wherein y=vec (Y), r=vec (R), vec () are vectorization function, i.e., pull into two-dimensional matrix by row one-dimensional
Column vector,For Kronecker product.The reception signal r of i-th of pulse of compressed sensing radariWith following sparse representation model
ri=Ψ θi (8)
Wherein Ψ is the sparse excessively complete dictionary of compressed sensing radar system, θiIt is received corresponding to signal for i-th of pulse
Sparse vector.Enable joint sparse vector theta=[θ1,θ2,...,θP] and its vectorization after data vectorThen
It can obtain
Wherein IPIndicate the unit matrix of a P × P.So far, the pulse accumulation observation of compressed sensing radar is just completed
The design of matrix and the sparse representation model for having obtained compression accumulation signal.
It, first will Γ points of perception matrix for the architectural characteristic of compressed sensing radar echo signal group its joint sparse vector
For J=L × M submatrix, wherein L, M is respectively the range cell number and doppler cells number that target information space is included.
The recognition function being defined as follows derives the building method of each submatrix,
Wherein ΓiTo perceive the i-th row of matrix Γ,For the A row of the B submatrix G, exist such as between A, B and i
Under correspondence
Column vector in the same submatrix both corresponds to same target information unit (same range cell and Doppler
Unit).In SMOMP algorithms, initialization first restores vectorWith residual vectorIn kth ' secondary subcycle
In, by residual errorWith each submatrix G(j)Correlation computations are done, and are chosen for the highest sub- square of the current residue degree of correlation
Battle array G(k′).Then update restores vector
Final updating residual vector
After K loop iteration, obtains K and chosen with the maximally related submatrix compositions of compression accumulation observation signal y
Matrix Ξ obtains the estimated value of joint sparse vector finally by least square methodThe detailed process of SMOMP algorithms such as table 1
It is shown.
Table 1SMOMP algorithm flows
Claims (7)
1. the compressed sensing radar single goal method for parameter estimation in the case of a kind of low signal-to-noise ratio, which is characterized in that including following
Step：
Step 1, the joint sparse for establishing compressed sensing radar echo pulse string indicates model, at sparse dictionary Ψ, i-th of arteries and veins
The reception signal r of punchingiWith following sparse representation model ri=Ψ θi, wherein θiFor the corresponding sparse vector of i-th of pulse；
Step 2, construction compressed sensing radar echo pulse compression accumulation observing matrix, it is real while compression receives data volume
Now to the accumulation of compressed sensing radar echo pulse；
Step 3, echo pulse signal group is observed using pulse compression observing matrix, obtains compression observation data；
Step 4, data carry out joint sparse reconstruct after observing pulse accumulation, obtain target component estimation.
2. the compressed sensing radar single goal method for parameter estimation in the case of low signal-to-noise ratio according to claim 1, special
Sign is that the construction of the compressed sensing radar echo pulse compression accumulation observing matrix in step 2 includes the following steps：
Step 2.1, compression sampling echo-signal is inputted；
Step 2.2, rough estimate target velocity obtains velocity estimation valuePair of goal-selling speed interval and pulse grouping simultaneously
It should be related to；
Step 2.3, according to speed interval where target, phase compensating factor is arranged with the average speed in the section；
Step 2.4, output grouping pulse accumulation observing matrix Φpulse。
3. according to claim 2 be based on compressed sensing radar single goal pulse accumulation observing matrix design method, spy
Sign is that the correspondence of goal-selling speed interval and pulse grouping, specifically includes in step 2.2：
Step 2.2.1, calculates the speed tolerant of compressed sensing radar, i.e. target rests on together within the time of P pulse persistance
Maximum speed in one range cellWherein TrIndicate the pulse persistance of compressed sensing radar system
Time, dmIndicate that the size of Range resolution unit, floor () are downward bracket function；
Step 2.2.2, calculating speed estimated valueUmber of pulse h in corresponding pulse grouping,
4. according to claim 2 be based on compressed sensing radar single goal pulse accumulation observing matrix design method, spy
Sign is, specific with the average speed setting phase compensating factor in the section according to speed interval where target in step 2.3
For：The base vector of pulse accumulation observing matrix is constituted using phase compensating factorIts
InThe corresponding Doppler frequency shift amount of average speed of speed interval where target.
5. according to claim 2 be based on compressed sensing radar single goal pulse accumulation observing matrix design method, spy
Sign is, pulse accumulation observing matrix Φ is exported in step 2.4pulseSpecially Φpulse=diag (β1uT,β2uT,...,βQU),
Wherein Q is pulse group number, i.e., the grouping number that echo-signal group obtains according to the prior information of target velocity, and Q=floor
(P/h), β1,β2,...,βQRespectively pulse group 1 is to the corresponding preferred coefficients of Q.
6. the compressed sensing radar single goal method for parameter estimation in the case of low signal-to-noise ratio according to claim 1, special
Sign is, utilizes pulse compression observing matrix to be observed echo pulse signal group in step 3, specially：
Assuming that receives echo-signal group R=[r1,r2,…,rP] include P echo impulse, wherein rpFor p-th of echo impulse, then
Compression accumulation observation signal Y is expressed as
Y=ΦdataRΦpulse
Wherein, ΦdataObserving matrix is compressed for data dimension, dimension is Z ' × Z, and Z is the length of original sampling data, and Z ' is compression
Data length afterwards, the i.e. compression ratio of data dimension are Z '/Z；
Two-dimensional observation problem in above formula can be equivalent to following one-dimensional observation problem
Wherein y=vec (Y), r=vec (R), vec () be vectorization function, i.e., by two-dimensional matrix by row pull into it is one-dimensional arrange to
Amount,For Kronecker product；Due to the reception signal r of i-th of pulseiWith following sparse representation model
ri=Ψ θi
Wherein Ψ is the sparse excessively complete dictionary of compressed sensing radar system, θiIt is received for i-th of pulse sparse corresponding to signal
Vector；Enable joint sparse vector theta=[θ1,θ2,...,θP] and its vectorization after data vectorIt can then obtain
It arrives
Wherein IPIndicate the unit matrix of a P × P.
7. the compressed sensing radar single goal method for parameter estimation in the case of low signal-to-noise ratio according to claim 1, special
Sign is, carries out joint sparse reconstruct to data after pulse accumulation observation in step 4, includes the following steps：
Step 4.1, it is J=L × M submatrix perception matrix Γ to be divided, and wherein L, M are respectively that target information space is included
Range cell number and doppler cells number；The recognition function being defined as follows derives the building method of each submatrix,
Wherein ΓiTo perceive the i-th row of matrix Γ,For the A row of the B submatrix G, exist as follows between A, B and i
Correspondence
A=floor (i/J)+1
B=mod (i/J)-floor ((A-1)/h)
Step 4.2：Initialize residual errorΞ0=0；
Step 4.3：It calculates
Step 4.4：Update Λ=Λ ∪ λ, Ξt=[Ξt-1,G(j)]；
Step 4.5：It is calculated using least square method
Step 4.6：Update residual errorT=t+1；
Step 4.7：Judge, if cycle-index meets t >=K, terminates cycle；Otherwise it gos to step and 4.2 continues cycling through.
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