CN108717189A - Bistatic MIMO radar imaging method based on compressive sensing theory - Google Patents
Bistatic MIMO radar imaging method based on compressive sensing theory Download PDFInfo
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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
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
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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
Abstract
The invention belongs to Radar Technology fields, disclose a kind of bistatic MIMO radar imaging method based on compressive sensing theory, including：Obtain radar return data；The ideal observing matrix under compressed sensing signal model is calculated；Using ideal observing matrix and radar return data, sparse reconstruct is carried out, obtains the sparse estimated value of echo signal；According to ideal observing matrix and the sparse estimated value of echo signal, actual observation matrix is determined；Sparse reconstruct is carried out using actual observation matrix, obtains the robust iterative value of echo signal；Iteration error value is calculated, if iteration error value is unsatisfactory for requiring, continues to obtain target signal estimation value and actual observation matrix according to current iteration, sparse reconstruct is carried out, until error amount is met the requirements.The present invention array there are range error and phase error when, remain able to obtain steady imaging results.
Description
Technical field
The invention belongs to Radar Technology fields, more particularly to the bistatic MIMO radar imaging side based on compressive sensing theory
Method, suitable for bistatic MIMO radar transmitting-receiving array element there are in the case of amplitude phase error signal model establish and it is sparse at
Picture.
Background technology
Radar imagery is one of most important task of modern radar.Compressive sensing theory can utilize Small Sample Database to obtain
Higher imaging resolution, therefore be widely used in radar imagery.
There is bistatic MIMO radar the advantage in many systems, bistatic MIMO radar can obtain more rich target and dissipate
Information and farther detection range are penetrated, can also be extended by virtual aperture and obtain higher resolution ratio.In addition, in practical war
The anti-interference ability and survival ability that can significantly improve radar system are split by cell site, receiving station under the environment of field.It will be double
Base MIMO radar and compressed sensing imaging theory are many notable in conjunction with having the advantages that.
However, when bistatic MIMO radar transmitting-receiving array there are when amplitude phase error, can seriously affect radar at
Image quality amount, therefore, it is current urgent problem to be solved that steady sparse imaging how is carried out under non-ideal model.
Invention content
In view of the above-mentioned problems, the present invention provides the bistatic MIMO radar imaging method based on compressive sensing theory, double
There are when amplitude phase error for base MIMO radar transmitting-receiving array element, it is proposed that a kind of new signal model, on the basis of this model
On, corresponding sparse recovery algorithms are deduced, it is hereby achieved that steady sparse imaging results.
In order to achieve the above objectives, the present invention is realised by adopting the following technical scheme：
A kind of bistatic MIMO radar imaging method based on compressive sensing theory, described method includes following steps：
Step 1, the echo-signal that bistatic MIMO radar receives is obtained, carrying out orthogonal matching to the echo-signal filters
Wave obtains radar return data；
Step 2, it determines that the transmitting of the bistatic MIMO radar is oriented to matrix and receives and is oriented to matrix；According to the transmitting
It is oriented to matrix and the reception is oriented to matrix, the ideal observing matrix under compressed sensing signal model is calculated；By the thunder
Up to echo data as in compressed sensing signal model output data, the ideal observing matrix is as compressed sensing signal mode
Observing matrix in type carries out sparse reconstruct, obtains the value according to a preliminary estimate of echo signal；
Iterations i is initialized：Enable i=1；
Step 3, according to the ideal observing matrix and (i-1)-th estimated value of echo signal, actual observation square is determined
Battle array；When i=1, (i-1)-th estimated value is to be worth according to a preliminary estimate；
Step 4, using the radar return data as output data, the actual observation in compressed sensing signal model
Matrix carries out sparse reconstruct as the observing matrix in compressed sensing signal model, obtains the ith estimated value of echo signal；
Step 5, the corresponding error amount of ith iteration is calculated, judges whether the error amount is less than preset error value：If institute
It states error amount and is more than preset error value, then i is enabled to add 1, and return to step 3；If the error amount is less than preset error value, basis
The ith estimated value of echo signal rebuilds scattering coefficient matrix, is drawn to get to described according to the scattering coefficient matrix
The target imaging figure of bistatic MIMO radar.
The present invention has the following advantages：(1) advantage with bistatic MIMO radar, passes through hair under practical battlefield surroundings
Penetrate station, receiving station splits the survival ability that can significantly improve radar, in addition, bistatic MIMO radar can obtain more rich mesh
Scattered information and farther detection range are marked, can also be extended by virtual aperture and obtain higher resolution ratio；(2) it is based on pressure
The principle of contracting perception imaging, obtains high imaging resolution using Small Sample Database, reduces computation complexity and hardware cost；
(3) steady imaging results still can be obtained there are when range error and phase error in array, there is practical application
Meaning.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is a kind of bistatic MIMO radar imaging method based on compressed sensing principle provided in an embodiment of the present invention
Flow diagram；
Fig. 2 is bistatic MIMO radar compressed sensing imaging system models schematic diagram；
Fig. 3 is original scene scatter times schematic diagram；
Fig. 4 is the error iterativecurve schematic diagram of imaging method provided in an embodiment of the present invention；
Fig. 5 (a) is the restoration result schematic diagram of imaging method provided in an embodiment of the present invention；
Fig. 5 (b) is the scattering point intensity schematic diagram of the recovery of imaging method provided in an embodiment of the present invention；
The imaging of Fig. 6 imaging methods provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm
Results contrast schematic diagram；Wherein, Fig. 6 (a) is the imaging results figure of direct compressed sensing imaging algorithm；Fig. 6 (b) is to have steadily and surely
The imaging results figure of imaging algorithm；Fig. 6 (c) is the imaging results figure of imaging method provided in an embodiment of the present invention；
Fig. 7 is the extensive of imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm
Multiple scattering point intensity schematic diagram；Wherein, Fig. 7 (a) is the scattering point intensity schematic diagram that direct compressed sensing imaging algorithm restores；
Fig. 7 (b) is the scattering point intensity schematic diagram for having steady imaging algorithm and restoring；Fig. 7 (c) is imaging provided in an embodiment of the present invention
The scattering point intensity schematic diagram that method is restored；
Fig. 8 is that imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm restore
The mean square error of signal is with amplitude phase error change schematic diagram；
Fig. 9 is that imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm restore
The signal degree of correlation is with amplitude phase error change schematic diagram；
Figure 10 is the partial enlarged view of Fig. 9.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 show a kind of bistatic MIMO radar imaging side based on compressed sensing principle provided in an embodiment of the present invention
The flow diagram of method.
As shown in Figure 1, the bistatic MIMO radar imaging method provided in an embodiment of the present invention based on compressed sensing principle,
Include the following steps；
Step 1, the echo-signal that bistatic MIMO radar receives is obtained, orthogonal matched filtering is carried out to echo-signal, is obtained
To radar return data.
Wherein, bistatic MIMO radar imaging system models are as shown in Figure 2.In Fig. 2, TX and RX are respectively radar emission battle array
Row and receiving array, θtAngle for target scene relative to emission array, θrAngle for target scene relative to receiving array
It spends, the regions Ω are exactly the two-dimensional grid divided in figure.
Biradical MIMO radar is configured to：Emit array element M, receives array element M, receive and dispatch array element spacingλ is transmitting
The wavelength of signal, wherein launch angle and receiving angle are divided into spends from 0 to 10, and lattice point size is 1 degree, i.e. θt∈[0°,
10 °], θr∈[0°,10°]。
Emit orthogonal waveforms in biradical MIMO radar imaging, if each transmitting of the emission array of the bistatic MIMO radar
Array element emits random frequency hopping signal, and the random frequency hopping signal emitted is narrow band signal, it is assumed that under q-th of pulse, mtA hair
Penetrating the signal that array element emits is：
WhereinIndicate the amplitude of signal,It obeys uniform on section (0, Q)
Distribution, andExpression rounds up, q=1,2 ..., Q, mt=1,2 ..., M, Q be code element number, M is transmitting element number of array, f
It is carrier frequency,It is stepped-frequency interval.M transmitting array element transmitting signal matrix be
Wherein,Wherein each element represents the intensity value of transmitting signal.
Specifically, step 1 specifically includes following sub-step：
(1a) obtains bistatic MIMO radar echo-signal
Wherein, q=1,2 ..., Q, ArIt is oriented to matrix, A for emission arraytIt is oriented to matrix, A for receiving arrayt=
[atp]M×P,Ar=[arp]N×P,
λ indicates signal wavelength, dtIndicate emission array
Array element spacing, drIndicate the array element spacing of receiving array；S indicates the transmitting signal waveform matrix of biradical MIMO radar, EqIt indicates
Noise matrix under q-th of pulse, YqIndicate the reception data matrix of q-th of pulse, θtpIndicate target scene relative to transmitting
The angle of array, θrpAngle for target scene relative to receiving array, XqIndicate scattering dot factor, EqIndicate that additive Gaussian is made an uproar
Sound.
(1b) is to bistatic MIMO radar echo-signal YqOrthogonal matched filtering obtains radar return data
Specifically, make matched filtering to receiving data, be the transposition for being multiplied by signal matrix to reception data, i.e.,：
Since transmitted waveform is orthogonal signalling, so there is SSH=I, so the later result of matched filtering can be obtained being：
Step 2, it determines that the transmitting of bistatic MIMO radar is oriented to matrix and receives and is oriented to matrix；It is oriented to matrix according to transmitting
It is oriented to matrix with receiving, the ideal observing matrix under compressed sensing signal model is calculated；Using radar return data as pressure
Output data, ideal observing matrix in contracting perceptual signal model are carried out as the observing matrix in compressed sensing signal model
Sparse reconstruct obtains the value according to a preliminary estimate of echo signal；Iterations i is initialized：Enable i=1.
Wherein, step 2 specifically includes following sub-step：
(2a) determines that the transmitting of bistatic MIMO radar is oriented to matrix At=[at1,at2,...,atp,...,atM] and receive
It is oriented to matrix Ar=[ar1,ar2,...,arp,...,arM]。
Wherein,Ar=[arp]N×P,
(2b) is oriented to matrix and receives according to transmitting is oriented to matrix, and the Concept of Ideal under compressed sensing signal model is calculated
Survey matrix
(2c) carries out vector quantization to the signal model of radar return data, obtains one-dimensional compressed sensing model：
Wherein, eq=vec (Eq)。
Herein, be based on the echo data obtained in step 1 it is data under two dimensional model, and the algorithm of sparse recovery is
One-dimensional algorithm, therefore, it is necessary to which two-dimensional data model is become one-dimensional data model, by the sparse recovery algorithms of compressed sensing
After, then restoration result become into two-dimensional matrix.
(2d) constructs convex optimization problem according to one-dimensional compressed sensing solving model：It utilizes
Convex optimization tool solves convex optimization problem, obtains the value according to a preliminary estimate of target
Wherein, | | | |1Expression takes l1Norm, | | | |2Expression takes l2Norm.
Above-mentioned optimization problem is a simple convex optimization problem, optimization tool packet (such as cvx) can be utilized to solve,
It can utilize and have algorithm (such as sparse recovery algorithms such as OMP, SLIM）It is solved.What solution obtainedIt is a G2×1
One-dimensional vector, it is become the matrix of G × G to get to the target scene X under ideal observing matrixqSparse recovery knot
Fruit.
Step 3, according to ideal observing matrix and (i-1)-th estimated value of echo signal, actual observation matrix is determined.
Wherein, when i=1, (i-1)-th estimated value is to be worth according to a preliminary estimate.
Wherein, step 3 specifically includes：
According to ideal observing matrix and (i-1)-th estimated value of echo signal, according to expression formula：
Determine actual observation matrix.
Wherein,Indicate actual observation matrix,Indicate that the radar return data after vector quantization, x indicate echo signal
(i-1)-th estimated value.
The theory deduction process of disadvantages mentioned above actual observation matrix is as follows：
Consider that array there are amplitude phase error, derives signal model at this time.When transmitting-receiving array is there are when amplitude phase error,
Ideal be oriented to before matrix has respectively been multiplied by a diagonal matrix ΓtAnd Γr.Specifically, there are the unreasonablys of array amplitude phase error
Want to receive and is oriented to matrix and sends guiding matrix and can be expressed as：
Wherein, ΑtAnd ΑrIt is that ideal transmitting-receiving is oriented to matrix,WithThere are the guiding squares of array amplitude phase error for representative
Battle array, ΓtAnd ΓrThe amplitude phase error matrix for respectively receiving and dispatching array element, is all diagonal matrix, and diagonal element expression is added in corresponding array element
Amplitude phase error yield value；Γt=diag [ρt1,...,ρti,...,ρtM],atiIt is in i-th of transmitting array element
Range error,It is the phase error in i-th of transmitting array element；Γr=diag [ζt1,...,ζti,...,ζtM],
ariIt is the range error in i-th of reception array element,It is the phase error in i-th of reception array element；Εt=Γt'ΑtTo assume
The non-ideal disturbance for being added in transmitting terminal is oriented to matrix, Εr=Γr'ΑrTo assume that the non-ideal disturbance for being added in receiving terminal is oriented to square
Battle array；
In this way, nonideal signal model is deformed intoThen it by both sides vector quantization, obtains：
Wherein,Α indicates known ideal observation square
Battle array, Ε indicate unknown perturbation matrix.
Because array is not the ideal observing matrix calculated there are amplitude phase error, actual observing matrix, cause to press
The non-ideal of contracting sensor model affects the accuracy of restoration result when sparse recovery.
Under above-mentioned non-ideal model, it is assumed that actual observing matrix Β=A+E, because E is unknown, it is possible to logical
It crosses and constructs following optimization problem, to solve the method for estimation of actual observation matrix：
Wherein, M and ε is constant, and Β is a matrix of variables.
In optimization problem, by constraining the relationship between Β and known ideal observation matrix A, to obtain physical presence
The optimal estimation of the observing matrix of disturbance.In the above optimization problem, Β and x are variable, are solved by the way of alternating iteration
The problem, i.e., the echo signal x solved according to step 2, brings the above optimization problem into, fixed in x, and construction glug is bright
Day function：
Wherein λ, μ are Lagrange multiplier, then seek partial derivative of the Lagrangian about Β：
Wherein first item：
Wherein, the mark of Tr [] representing matrix, subscript H expressions take conjugate transposition symbol.
Section 2：
Section 3：
Enable partial derivativeIt is 0, you can obtain the estimation formulas of actual observation matrix：
When obtaining radar return data, and using receive data and ideal observation matrix A obtain preliminary sparse solution x with
Afterwards, so that it may to estimate the observing matrix of physical presence disturbance using these data.
Step 4, using radar return data as in compressed sensing signal model output data, actual observation matrix as
Observing matrix in compressed sensing signal model carries out sparse reconstruct, obtains the ith estimated value of echo signal.
Wherein, step 4 specifically includes：
Build optimization problem：And it is carried out using radar return data and actual observation matrix
Sparse recovery obtains the ith estimated value of echo signal：
It is solved with convex optimization problem using cvx kits, obtains steady sparse restoration result.The observing matrix acquiredThan ideal observing matrix A closer to actual observing matrix, so utilizingWithThe sparse solution combined is closer
Actual echo signal, restoration result are more steady.
Step 5, the corresponding error amount of ith iteration is calculated, whether error in judgement value is less than preset error value：If error amount
More than preset error value, then i is enabled to add 1, and return to step 3；If error amount is less than preset error value, according to the of echo signal
I estimated value rebuilds scattering coefficient matrix, is drawn to get to the target of bistatic MIMO radar according to scattering coefficient matrix
Image.
Wherein, the corresponding error amount of ith iteration is
That is, the x obtained according to thisqWithCalculate error amountIf the error amount less than given value,
Then stop calculating, by one-dimensional sparse signal xqIt is redeveloped into two-dimensional matrixXqThe as scattering system of targeted imaging region
Matrix number, to XqIt draws, that is, obtains imaging results；If the error amount of this result of calculation is more than given value, return
Step 3, the new sparse signal x obtained according to current iterationqWith radar return dataReevaluate actual observation matrix
And then sparse reconstruct is carried out using the actual observation matrix reevaluated, obtain new xq, until error amount is met the requirements.
Bistatic MIMO radar imaging method provided in an embodiment of the present invention based on compressive sensing theory, based on compression sense
The principle for knowing imaging obtains high imaging resolution using Small Sample Database, can reduce computation complexity and hardware cost, and
In array there are when range error and phase error, steady imaging results still can be obtained, there is practical application meaning
Justice.
The effect of the above method provided in an embodiment of the present invention is verified below by way of emulation experiment：
1, emulation experiment data：
There are two Scattering Targets for setting, DOA the and DOD angle values of target are respectively (2 °, 9 °) and (9 °, 2 °), and will be dissipated
The scattering coefficient for penetrating target is set as 1, and biradical ditch is set as 90 °.Raw scattered point model is as shown in figure 3, can from Fig. 3
Go out, two point targets are distributed on pre-set position.
2, emulation experiment content：
Three groups of experiments are carried out respectively：
Experiment 1 is 10dB in signal-to-noise ratio, and it is 1 to receive data number of snapshots, under conditions of amplitude phase error power is 0.4, is used
The method that the embodiment of the present invention proposes is imaged, and the correctness of the method for the present invention is verified.
Fig. 4 is shown to be illustrated using the sparse error iterativecurve for restoring signal of imaging method provided in an embodiment of the present invention
Figure.From fig. 4, it can be seen that chosen in parameter it is suitable, by about 20 iteration, it is provided in an embodiment of the present invention at
Image space method can restrain.
Fig. 5 show the imaging results figure using imaging method provided in an embodiment of the present invention, wherein Fig. 5 (a) is this hair
The restoration result schematic diagram for the imaging method that bright embodiment provides, Fig. 5 (b) are the extensive of imaging method provided in an embodiment of the present invention
Multiple scattering point intensity schematic diagram.
From Fig. 5 (a) as can be seen that the method for the present invention can be correctly restored the position of target scattering point, provide clearly
Point imaging results；It can be seen that two scattering points are well separated from Fig. 5 (b), and secondary lobe is very low.This shows this hair
Bright method is ideal in the imaging results of small sample, larger amplitude phase error power.
Experiment 2 is 10dB in signal-to-noise ratio, and it is 1 to receive data number of snapshots, and range error power is 0.2, phase error power
Under conditions of 0.3, be imaged using method provided in an embodiment of the present invention, and with conventional compact perceive imaging algorithm with
And existing steady compressed sensing algorithm is compared.
The imaging of Fig. 6 imaging methods provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm
Results contrast schematic diagram；Wherein, Fig. 6 (a) is the imaging results figure of direct compressed sensing imaging algorithm；Fig. 6 (b) is to have steadily and surely
The imaging results figure of imaging algorithm；Fig. 6 (c) is the imaging results figure of imaging method provided in an embodiment of the present invention.
Fig. 7 is the extensive of imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm
Multiple scattering point intensity schematic diagram；Wherein, Fig. 7 (a）The scattering point intensity schematic diagram restored for direct compressed sensing imaging algorithm；
Fig. 7 (b）To have the scattering point intensity schematic diagram that steady imaging algorithm restores；Fig. 7 (c）For imaging provided in an embodiment of the present invention
The scattering point intensity schematic diagram that method is restored.
From fig. 6, it can be seen that due to the use of data only there are one number of snapshots, so leading to the extensive of existing robust algorithm
Multiple junction fruit is even not as good as the restoration result of direct compressed sensing algorithm.From figure 7 it can be seen that under Small Sample Database, the present invention
Method has restoration result and the lower secondary lobe closer to original object scene than existing algorithm.
Experiment 3, enables amplitude phase error power equal, and from 0 to 0.6, is divided into 0.01 carry out value, each amplitude phase error work(
Circulating repetition is tested 10 times under rate situation, to the method for the present invention, conventional compact perception imaging algorithm and existing steady compression
Perception algorithm carry out Monte Carlo Experiment, verification algorithm performance with array amplitude phase error situation of change.
In this emulation experiment, by the way of two kinds of measure algorithm performances, mode one is using restoring signal and original
The error amount of signal is as measurement standard：
Wherein, xreIt indicates to restore signal, x indicates original signal；
Mode is second is that using the degree of correlation of signal and original signal is restored as measurement standard：
According to defined above, for an algorithm, the MSE for restoring signal is smaller, and γ values are bigger, indicate its restoration result
Accuracy it is higher.
Fig. 8 is that imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm restore
The mean square error of signal is with amplitude phase error change schematic diagram.
Curve from according to Fig. 8 can be seen that under statistical significance, and the method for the present invention is extensive compared to direct compressed sensing
Compound method and existing robust algorithm have lower recovery MSE；
Fig. 9 is that imaging method provided in an embodiment of the present invention and direct compressed sensing algorithm and existing robust algorithm restore
The signal degree of correlation is with amplitude phase error change schematic diagram；Figure 10 is the partial enlarged view of Fig. 9.
The signal restoration result that can be seen that the method for the present invention from Fig. 9 and Figure 10 has the higher signal degree of correlation, and
When array amplitude phase error changes, the method for the present invention has higher robustness.
To sum up, the correctness, validity and reliability of the method for the present invention are demonstrated by above-mentioned emulation experiment.
One of ordinary skill in the art will appreciate that：Realize that all or part of step of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in computer read/write memory medium, which exists
When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes：ROM, RAM, magnetic disc or CD
Etc. the various media that can store program code.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (6)
1. a kind of bistatic MIMO radar imaging method based on compressive sensing theory, which is characterized in that the method includes such as
Lower step：
Step 1, the echo-signal that bistatic MIMO radar receives is obtained, orthogonal matched filtering is carried out to the echo-signal, is obtained
To radar return data；
Step 2, it determines that the transmitting of the bistatic MIMO radar is oriented to matrix and receives and is oriented to matrix；It is oriented to according to the transmitting
Matrix and the reception are oriented to matrix, and the ideal observing matrix under compressed sensing signal model is calculated；The radar is returned
Wave number according to as in compressed sensing signal model output data, the ideal observing matrix is as in compressed sensing signal model
Observing matrix, carry out sparse reconstruct, obtain the value according to a preliminary estimate of echo signal；
Iterations i is initialized：Enable i=1；
Step 3, according to the ideal observing matrix and (i-1)-th estimated value of echo signal, actual observation matrix is determined；i
When=1, (i-1)-th estimated value is to be worth according to a preliminary estimate；
Step 4, using the radar return data as the output data in compressed sensing signal model, the actual observation matrix
As the observing matrix in compressed sensing signal model, sparse reconstruct is carried out, obtains the ith estimated value of echo signal；
Step 5, the corresponding error amount of ith iteration is calculated, judges whether the error amount is less than preset error value：If the mistake
Difference is more than preset error value, then i is enabled to add 1, and return to step 3；If the error amount is less than preset error value, according to target
The ith estimated value of signal rebuilds scattering coefficient matrix, is drawn to get to described biradical according to the scattering coefficient matrix
The target imaging figure of ground MIMO radar.
2. according to the method described in claim 1, it is characterized in that, step 1 specifically includes following sub-step：
(1a) obtains bistatic MIMO radar echo-signalWherein q=1,2 ..., Q, ArFor transmitting
Array is oriented to matrix, AtIt is oriented to matrix, A for receiving arrayt=[atp]M×P,
Ar=[arp]N×P,
λ indicates signal wavelength, dtIndicate emission array
Array element spacing, drIndicate the array element spacing of receiving array；S indicates the transmitting signal waveform matrix of biradical MIMO radar, EqIndicate the
Noise matrix under q pulse, YqIndicate the reception data matrix of q-th of pulse, θtpIndicate target scene relative to transmitting battle array
The angle of row, θrpAngle for target scene relative to receiving array；
(1b) is to the bistatic MIMO radar echo-signal YqOrthogonal matched filtering obtains radar return data
3. according to the method described in claim 1, it is characterized in that, step 2 specifically includes following sub-step：
(2a) determines that the transmitting of the bistatic MIMO radar is oriented to matrix At=[at1,at2,...,atp,...,atM] and receive
It is oriented to matrix Ar=[ar1,ar2,...,arp,...,arM]；
Wherein,Ar=[arp]N×P,
(2b) is oriented to matrix according to the transmitting and the reception is oriented to matrix, and the reason under compressed sensing signal model is calculated
Think observing matrix
(2c) carries out vector quantization to the signal model of radar return data, obtains one-dimensional compressed sensing model：
Wherein, eq=vec (Eq)；
(2d) constructs convex optimization problem according to the one-dimensional compressed sensing solving model：It utilizes
Convex optimization tool solves the convex optimization problem, obtains the value according to a preliminary estimate of target
4. according to the method described in claim 1, it is characterized in that, step 3 specifically includes：
According to the ideal observing matrix and (i-1)-th estimated value of echo signal, according to expression formula：
Determine actual observation matrix；
Wherein,Indicate actual observation matrix,Indicate that the radar return data after vector quantization, x indicate the (i-1)-th of echo signal
Secondary estimated value.
5. according to the method described in claim 1, it is characterized in that, step 4 specifically includes：
Build optimization problem：And it is carried out using the radar return data and actual observation matrix
Sparse recovery obtains the ith estimated value of echo signal：
6. according to the method described in claim 5, it is characterized in that, the corresponding error amount of ith iteration is
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