CN107329117A - It is a kind of that compensation method is composed based on the bistatic airborne radar self-adapting clutter for improving OMP - Google Patents

It is a kind of that compensation method is composed based on the bistatic airborne radar self-adapting clutter for improving OMP Download PDF

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CN107329117A
CN107329117A CN201710427524.2A CN201710427524A CN107329117A CN 107329117 A CN107329117 A CN 107329117A CN 201710427524 A CN201710427524 A CN 201710427524A CN 107329117 A CN107329117 A CN 107329117A
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mrow
msub
mtr
clutter
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沈明威
纪存孝
陶震
张琪
王冠
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Hohai University HHU
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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
    • G01S7/28—Details of pulse systems
    • G01S7/2813—Means providing a modification of the radiation pattern for cancelling noise, clutter or interfering signals, e.g. side lobe suppression, side lobe blanking, null-steering arrays
    • 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
    • G01S13/003—Bistatic radar systems; Multistatic radar systems

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a kind of based on the bistatic airborne radar self-adapting clutter spectrum compensation method for improving OMP, orthogonal matched jamming abbreviation OMP, the invention for bistatic airborne radar clutter apart from it is non-stationary the problem of, the Doppler frequency of main-lobe clutter is estimated first, the spatial frequency of main-lobe clutter is obtained by improving OMP again, and then different distance unit main-lobe clutter is compensated respectively.The simulation experiment result shows, after adaptive equalization, and bistatic airborne radar clutter makes moderate progress apart from non-stationary, and triple channel joint self-adaptive processing (3DT) improvement factor improves about 6dB in main-lobe clutter area.Adaptive equalization effect of the present invention is good, operand is small, it is easy to engineering construction.

Description

It is a kind of that compensation method is composed based on the bistatic airborne radar self-adapting clutter for improving OMP
Technical field
It is particularly a kind of based on the biradical airborne of improvement OMP the present invention relates to biradical clutter space-time spectrum compensation technique field Radar self-adaption clutter spectrum compensation method.
Background technology
In recent years, motion platform bistatic detection obtains the extensive concern of domestic and international scientific research institution.Bistatic airborne radar Using the system of bistatic, away from danger zone, receiver is placed in area of interest to emitter, is visited than general single base Examining system has the advantages that safe, operating distance is remote, strong anti-interference performance.Space-time adaptive processing (STAP) is airborne list The advanced technology that base radar clutter suppresses and moving-target is detected, relatively broad applies on AEW surveillance radar.At present Applications of the STAP on bistatic airborne detection system is also in theoretical research stage, and STAP is in bistatic airborne detection system Application also have many open questions.
Bistatic Airborne Radar Detection System is due to bistatic, its clutter angle-Doppler's two-dimensional spectrum complex distribution； Biradical distance is different with biradical geometric configuration and changes over time, and biradical clutter has stronger distance non-stationary, Suppression of the STAP to biradical clutter is also just more difficult, and this is accomplished by carrying out the clutter of different distance unit with special method Compensation.In airborne single base radar, solve clutter has Doppler frequency shift (DW) apart from non-stationary method, and angle Doppler mends Repay (ADC), adaptive angle Doppler effect correction (A2DC) etc..DW, ADC compensating parameter are directly counted according to radar system parameters Calculate, poor-performing is compensated under error；AADC uses minimum variance distortionless response (MVDR) Power estimation compensating parameter, but sub-aperture Footpath smoothly reduces the resolution ratio of MVDR spectrums and operand is very big.
The content of the invention
The technical problems to be solved by the invention are to overcome the deficiencies in the prior art and provide a kind of based on pair for improving OMP Base airborne radar self-adapting clutter composes compensation method, and present invention improves the stronger distance of biradical clutter spectrum is non-stationary.
The present invention uses following technical scheme to solve above-mentioned technical problem：
According to proposed by the present invention a kind of based on the bistatic airborne radar self-adapting clutter spectrum compensation method for improving OMP, bag Include following steps：
Step 1: main-lobe clutter Doppler-frequency estimation, is specially：
If it is N that bistatic airborne radar reception system antenna array elements number, which is the spatial domain free degree, in a coherent pulse product Time domain impulse number is K in tired CPI, and l-th of range cell receives signal Pulse by Pulse and be arranged as Xl, to XlCarry out fast Fourier change FFT is changed, all doppler cells output signal D of l-th of range cell are obtainedl：
Wherein, FDFor FFT matrix, subscript H represents transposition, Dl_iFor l-th of range cell, i-th of doppler cells Output signal, 1≤i≤K；To XlReceived and wave beam Σ by digital beam froming DBF formationl, ΣlIt is expressed as：
Σl=wΣXl
Wherein, wΣFor reception and wave beam weight；
To reception and wave beam ΣlFFT is carried out, all doppler cells of l-th of range cell is obtained and receives and wave beam Output signal DΣ,l：
Wherein, FΣ,DFor with wave beam FFT matrix, DΣ,l_iReceived for l-th range cell, i-th of doppler cells and Beamformer output signal；For l-th of range cell, DΣ,lIn maximum absolute value value where doppler cells be designated as l_d_ Max, l_d_max correspond to the Doppler frequency f of receiver main-lobe clutterd_l；
Step 2: main-lobe clutter spatial frequency is estimated：In l-th of range cell, to D in step onelIn be in l_d_max The doppler cells output signal namely maximum Doppler element output signal D of positionl_d_maxThe sparse reconstruct in spatial domain is carried out, then is led to Crossing center of gravity blending algorithm, accurately calculating obtains main-lobe clutter spatial frequency, the maximum Doppler element output signal in sparse reconstruct Dl_d_maxFor measurement vector；Specially：
2.1st, iterative parameter initial value is set：Residual vector initial value r0=Dl_d_max, degree of rarefication initial value S=1, iteration Count number initial value t=1, supported collection initial value Ω0For empty setAllowable error is||·||2For L2Model Number computing；The clutter spatial domain amplitude distribution for obtaining sparse reconstruct by the t times iterative estimate isIts initial valueM is N ×NsThe observing matrix of dimension, and M is one group of super complete base being made up of spatial domain steering vector, NsFor spatial domain quantifying unit number, μpTable Show observing matrix M pth row, 1≤p≤Ns；
2.2nd, supported collection updates：According toFind out observing matrix M and residual vector rt-1The row μ of correlation maximumpt, and update supported collection
Wherein, |<rt-1,μp>| to seek rt-1And μpThe absolute value of inner product,When taking maximum for function f (x) Corresponding variable x value,It is to ask in the t times iteration to make rt-1And μpInner product it is absolute Row when value is maximumΩtThe supported collection obtained for the t times iteration, by preceding t-1 supported collection Ωt-1WithThe new collection constituted Close,For Ωt-1And rowUnion；
2.3rd, sparse reconstruct updates：The clutter spatial domain obtained using the t times iterative estimate of least-squares estimation Algorithm for Solving Amplitude distribution
Wherein,Corresponding variable x value when taking minimum value for function f (x), It is to ask in the t times iterationWhen taking minimum valueValue；
2.4th, residual vector updates：
2.5th, iteration is adjudicated：By residual vector rtWith allowable error δlMake decisions, if | | rt||2＞ δl, then it is iterated Estimation, t=t+1, linearly increasing degree of rarefication S value is S=S+1, repeat step 2.2 to step 2.4；If | | rt||2≤δl, then Stop iteration, complete sparse reconstruct, perform step 2.6；
2.6th, in last time iterative estimate, terminate to remember during sparse reconstructσl_d_maxAs l-th away from From unit maximum Doppler element output signal Dl_d_maxThe spatial domain amplitude distribution of the improved sparse reconstruct of OMP algorithms；
L-th of range cell main-lobe clutter spatial frequency f is obtained using center of gravity blending algorithms_l：
Wherein, NqObtained σ is reconstructed to be sparsel_d_maxMain-lobe clutter scattering unit number, fs,qFor σl_d_maxQ-th of main lobe The spatial frequency of clutter scattering unit, σl_d_max,qFor σl_d_maxQ-th of main-lobe clutter scattering unit range value；
Step 3: main-lobe clutter adaptive equalization, is specially：
The spectral centroid of the main-lobe clutter of l-th of the range cell obtained by step one and step 2 is (fs_l,fd_l), if The spectral centroid of detecting distance unit main-lobe clutter is (fs_0,fd_0), fs_0And fd_0Respectively the detecting distance unit main lobe is miscellaneous The difference Δ f of the spatial frequency and Doppler frequency of ripple, then l-th of range cell and the Doppler frequency of detecting distance unitd_l、 The difference Δ f of spatial frequencys_lRespectively：
Δfd_l=fd_l-fd_0
Δfs_l=fs_l-fs_0
Therefore, the normalization Doppler frequency compensation factor T of l-th of range cellt_lWith spatial frequency compensating factor Ts_l Respectively：
Wherein, frFor pulse recurrence frequency, j is imaginary unit；
The data of l-th of range cell are after adaptive equalization：
Cl=Ts_lXlTt_l。
Enter one as a kind of bistatic airborne radar self-adapting clutter spectrum compensation method based on improvement OMP of the present invention Walk prioritization scheme, XlIt is expressed as follows：
Wherein, Sl_iSignal phasor, 1≤i≤K are received for each array element of l-th of range cell, i-th of pulse.
Enter one as a kind of bistatic airborne radar self-adapting clutter spectrum compensation method based on improvement OMP of the present invention Walk prioritization scheme, N=16.
Enter one as a kind of bistatic airborne radar self-adapting clutter spectrum compensation method based on improvement OMP of the present invention Walk prioritization scheme, K=64.
Enter one as a kind of bistatic airborne radar self-adapting clutter spectrum compensation method based on improvement OMP of the present invention Walk prioritization scheme, Ns=128.
The present invention uses above technical scheme compared with prior art, with following technique effect：
(1) by estimating the Doppler frequency and spatial frequency of main-lobe clutter, each range cell clutter is carried out adaptive Compensation, improve the stronger distance of biradical clutter non-stationary；
(2) the simulation experiment result shows, the adaptive compensation operation amount of the biradical clutter spectrum based on improvement OMP is smaller, mend Repay effect good, it is easy to engineering construction.
Brief description of the drawings
Fig. 1 is bistatic airborne radar geometrical relationship schematic diagram；
Fig. 2 a are different distance unit main-lobe clutter azimuthal variation trajectory diagram；
Fig. 2 b are different distance unit main-lobe clutter Doppler frequency variation track figure；
Fig. 3 is signal processing flow figure of the present invention；
Fig. 4 is improvement OMP algorithm flow block diagrams；
Fig. 5 a are biradical front and rear flight clutter space-time two-dimensional spectrum；
The biradical clutter two-dimensional spectrum that Fig. 5 b obtain for the sparse reconstruct of convex optimization；
To improve, OMP is sparse to reconstruct the biradical clutter two-dimensional spectrum obtained to Fig. 5 c；
Fig. 6 a are main-lobe clutter centrode figure before compensation；
Fig. 6 b are main-lobe clutter centrode figure after the adaptive equalization based on convex optimization；
Fig. 6 c are based on main-lobe clutter centrode figure after the adaptive equalization for improving OMP；
Fig. 7 is the improvement factor curve map of 3DT before and after compensation.
Embodiment
Technical scheme is described in further detail below in conjunction with the accompanying drawings：
Assuming that R-T unit is all positive side view battle array Phased Array Radar Antenna, as shown in figure 1, the throwing with receiver on the ground Shadow point O is that the origin of coordinates sets up coordinate system, is respectively z-axis, x-axis perpendicular to the heading of ground upwardly direction, receiver, The direction vertical with x-axis is y-axis on level ground.Emitter in the horizontal plane be projected as Q points, the height point of R-T unit Wei not HrAnd Ht, the distance of the subpoint of O, Q two is d on groundtr, the line and x-axis positive direction angle that 2 points of O, Q are β1, emitter Heading is β2。β1、β2It is zero when being overlapped with x-axis, is counterclockwise positive direction.Assuming that R-T unit works asynchronously, i.e., it is same The rang ring of distance samples is the intersection of ellipsoid and earth surface, the transmission path R in same rang ringtWith reception Path RrSum is fixed value Rl.It has been generally acknowledged that in a CPI, biradical geometrical relationship is constant.Biradical machine is mainly studied herein Carry clutter self-adapting compensation and suppression of the radar under front and rear flight configuration, i.e. β1=0, β2=0.
Usually, the clutter Doppler frequency of biradical airborne detection system can be expressed as：
Wherein, θt(θr)、And vt(vr) be respectively transmitting antenna (reception antenna) azimuth, the angle of pitch and transmitting Machine (receiver) flying speed, λ is radar wavelength.The main lobe of transmitting antenna is pointed in the present inventionWith reference to above formula, The corresponding Doppler frequency of main-lobe clutter can be reduced to：
When transmitting antenna main lobe points to 90 °, Fig. 2 a give the rail that different distance unit receives main lobe bearing sense angle Mark, Fig. 2 b give the track of different distance unit main lobe Doppler frequency.Under the configuration of biradical front and rear flight, reception antenna Main lobe point to pointed to transmitting antenna main lobe it is misaligned, short range clutter area reception main lobe azimuth with stronger apart from space-variant, with This variation tendency of increase of biradical distance is gradually delayed.
What the present invention was studied includes Doppler-frequency estimation, sky based on the biradical clutter self-adapting backoff algorithm for improving OMP Between Frequency Estimation and adaptive equalization.Lower Fig. 3 is the signal processing flow figure of the present invention.Orthogonal matched jamming abbreviation OMP.
1st, main-lobe clutter Doppler-frequency estimation, be specially：
Assuming that it is N that bistatic airborne radar reception system antenna array elements number, which is the spatial domain free degree, in a coherent pulse Accumulation (CPI) interior time domain impulse number is K, and l-th of range cell receives signal Pulse by Pulse and be arranged as Xl, it is expressed as follows：
Wherein, Sl_iSignal phasor, 1≤i≤K, to X are received for each array element of l-th of range cell, i-th of pulselCarry out fast Fast Fourier transformation (FFT), obtains all doppler cells output signal D of l-th of range celll：
Wherein, FDFor FFT matrix, subscript H represents transposition, Dl_iFor l-th of range cell, i-th of doppler cells Output signal；To XlReceived and wave beam Σ by digital beam froming (DBF) formationl, it is expressed as：
Σl=wΣXl
Wherein, wΣFor reception and wave beam weight；To reception and wave beam ΣlFFT is carried out, l-th of range cell is obtained All doppler cells are received and beamformer output signal DΣ,l：
Wherein, FΣ,DFor with wave beam FFT matrix, DΣ,l_iReceived for l-th range cell, i-th of doppler cells and Beamformer output signal；For l-th of range cell, DΣ,lIn maximum absolute value value where doppler cells (be designated as l_d_ Max it) correspond to the Doppler frequency f of receiver main-lobe clutterd_l；
2nd, Fig. 4 is improves OMP algorithm flow block diagrams, and main-lobe clutter spatial frequency is estimated：In l-th of range cell, to step D in rapid onelIn in l_d_max positions doppler cells output signal namely maximum Doppler element output signal Dl_d_max The sparse reconstruct in spatial domain is carried out, then accurately calculating obtains main-lobe clutter spatial frequency by center of gravity blending algorithm, in sparse reconstruct Maximum Doppler element output signal Dl_d_maxFor measurement vector；Specially：
2.1st, iterative parameter initial value is set：Residual vector initial value r0=Dl_d_max, degree of rarefication initial value S=1, iteration Count number initial value t=1, supported collection initial value Ω0For empty setAllowable error is||·||2For L2Model Number computing；The clutter spatial domain amplitude distribution for obtaining sparse reconstruct by the t times iterative estimate isIts initial valueM is N ×NsThe observing matrix of dimension, and M is one group of super complete base being made up of spatial domain steering vector, NsFor spatial domain quantifying unit number, μpTable Show observing matrix M pth row (1≤p≤Ns)；Dl_d_maxFor measurement vector；
2.2nd, supported collection updates：According toFind out observing matrix M and residual vector rt-1 The row of correlation maximumAnd update supported collection
Wherein, |<rt-1,μp>| to seek rt-1And μpThe absolute value of inner product,When taking maximum for function f (x) Corresponding variable x value,It is to ask in the t times iteration to make rt-1And μpInner product it is absolute Row when value is maximumΩtThe supported collection obtained for the t times iteration, by preceding t-1 supported collection Ωt-1WithThe new collection constituted Close,For Ωt-1And rowUnion；
2.3rd, sparse reconstruct updates：The clutter spatial domain obtained using the t times iterative estimate of least-squares estimation Algorithm for Solving Amplitude distribution
Wherein,Corresponding variable x value when taking minimum value for function f (x), It is to ask in the t times iterationWhen taking minimum valueValue；
2.4th, residual vector updates：
2.5th, iteration is adjudicated：By residual vector rtWith allowable error δlMake decisions, if | | rt||2＞ δl, then it is iterated Estimation, t=t+1, linearly increasing degree of rarefication S value is S=S+1, repeat step 2.2 to step 2.4；If | | rt||2≤δl, then Stop iteration, complete sparse reconstruct, perform step 2.6；
2.6th, in last time iterative estimate, terminate to remember during sparse reconstructσl_d_maxAs l-th away from From unit maximum Doppler element output signal Dl_d_maxThe spatial domain amplitude distribution of the improved sparse reconstruct of OMP algorithms；
L-th of range cell main-lobe clutter spatial frequency f is obtained using center of gravity blending algorithms_l：
Wherein, NqObtained σ is reconstructed to be sparsel_d_maxMain-lobe clutter scattering unit number, fs,qFor σl_d_maxQ-th of main lobe The spatial frequency of clutter scattering unit, σl_d_max,qFor σl_d_maxQ-th of main-lobe clutter scattering unit range value；
Main-lobe clutter spatial frequency only needs to carry out maximum Doppler element output signal the sparse reconstruct in spatial domain again by weight Heart blending algorithm is accurately calculated and obtained；
3rd, main-lobe clutter adaptive equalization, be specially：
It is (f by the spectral centroid of the above-mentioned main-lobe clutter for obtaining l-th of range cells_l,fd_l), if detecting distance unit The spectral centroid of main-lobe clutter is (fs_0,fd_0), fs_0And fd_0The respectively spatial frequency of the detecting distance unit main-lobe clutter And the difference Δ f of Doppler frequency, then l-th of range cell and the Doppler frequency of detecting distance unitd_l, spatial frequency difference Δfs_lRespectively：
Δfd_l=fd_l-fd_0
Δfs_l=fs_l-fs_0
Therefore, the normalization Doppler frequency compensation factor T of l-th of range cellt_lWith spatial frequency compensating factor Ts_l Respectively：
Wherein, frFor pulse recurrence frequency；
The data of l-th of range cell are after adaptive equalization：
Cl=Ts_lXlTt_l；
After adaptive equalization, clutter recognition is carried out with 3DT algorithms.3DT is Doppler where joint target to be detected Unit and both sides neighboring Doppler unit carry out self-adaptive processing, and it does not carry out dimension-reduction treatment, 3DT degree of freedom in systems in spatial domain For 3N.Compare the validity and performance of backoff algorithm finally by the change curve of 3DT algorithm improvement factors before and after compensation, change Kind factor expression is as follows：
Wherein, SCNRinFor letter miscellaneous noise ratio, SCNR before compensationoutTo believe miscellaneous noise ratio after compensation.
The performance of inventive algorithm is verified below by Computer Simulation.Bistatic airborne radar system emulation parameter such as table Shown in 1, time domain impulse number K=64 in uniform linear array array number N=16, a CPI.In emulation experiment, in order to contrast, adopt Sparse reconstruct is carried out with the high convex optimization of arithmetic accuracy and improved OMP.Using the 150th range cell as detection unit, to 51 to the 250th range cell main-lobe clutters are compensated, and calculate corresponding 3DT algorithms improvement factor.
The bistatic Airborne Radar Detection System simulation parameter of table 1
Carrier frequency f0 1.25GHz
Pulse recurrence frequency fr 4200Hz
Time sampling frequency fs 2MHz
Earth radius Re 6378.16km
Array element spacing and wavelength ratio 1/2
Emitter height Ht 10km
Receiver height Hr 6km
The air line distance d of dual-mode antennatr 10km
Azimuth firing angle θt 0.5π
Receive azimuth angle thetarSpan 0~2 π
Biradical location parameter β1Value 0
Emitter heading β2Value 0
Emitter speed vt 150m/s
Receiver speed vr 200m/s
Fig. 5 a are clutter angle-Dopplergram of the 150th range cell, and Fig. 5 b are the clutters with the convex sparse reconstruct of optimization Space-time spectrum, sparse reconstruct is carried out as shown in Figure 5 c using improved OMP.Comparison diagram 5b and Fig. 5 c, the improved sparse reconstruct spectrums of OMP Reconstruct spectrum more sparse than convex optimization is slightly wider, but can be prevented effectively from the high secondary lobe and low resolution of spatial domain fourier spectrum.
Fig. 6 a give compensation before each range cell main-lobe clutter centrode curve, biradical clutter spectrum center with away from From and change, there is stronger distance non-stationary；Fig. 6 b, 6c give the compensation based on convex optimization, the improvement sparse reconstruct of OMP Each range cell main-lobe clutter centrode curve afterwards, it can be seen that after adaptive equalization, in each range cell main-lobe clutter The heart is nearly seated at same position.
Fig. 7 gives 3DT before compensation, based on convex optimization and based on 3DT improvement factors after the compensation for improving the sparse reconstruct of OMP Curve, after adaptive equalization, the recess of 3DT processing narrows；Based on convex optimization and based on the adaptive of the improvement sparse reconstruct of OMP About 6.58dB and 6.04dB has been respectively increased in IF curves main lobe area after compensation.
When i L range cell maximum Doppler unit of correspondence, convex sparse L maximum Doppler unit of reconstruct of optimization Operand is O [Ns 2NL]；Assuming that degree of rarefication is S after each sparse reconstruct of range cell maximum Doppler unitl_imax, then OMP is improved Operand beWith reference to Fig. 5 c and the sparse reconstruct situation of other range cell clutter spectrums, It is 4 to take L range cell principal maximum doppler cells degree of rarefication average S, then improves the operand of OMP algorithms and can be reduced toSpatial domain quantifying unit number N in the present inventions=128, therefore, reconstruct L range cell maximum Doppler The operand that OMP algorithms are improved during unit is only the 7.81% of convex optimized algorithm, and clutter self-adapting compensation effect is preferable.
Improve the IF curves after OMP algorithm compensations and have dropped 0.5dB than convex optimized algorithm in main lobe area, but improve OMP's Operand is only the 7.81% of convex optimization operand.Therefore, can effectively it be changed based on the adaptive compensation algorithm for improving OMP in engineering The clutter of kind bistatic airborne radar improves rejections of the STAP to main-lobe clutter apart from non-stationary.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert The specific implementation of the present invention is confined to these explanations.For general technical staff of the technical field of the invention, On the premise of not departing from present inventive concept, some simple deductions or replacement can also be made, should all be considered as belonging to the present invention's Protection domain.

Claims (5)

1. a kind of compose compensation method based on the bistatic airborne radar self-adapting clutter for improving OMP, it is characterised in that including following step Suddenly：
Step 1: main-lobe clutter Doppler-frequency estimation, is specially：
If it is N that bistatic airborne radar reception system antenna array elements number, which is the spatial domain free degree, in a coherent pulse accumulation CPI Interior time domain impulse number is K, and l-th of range cell receives signal Pulse by Pulse and be arranged as Xl, to XlFast Fourier Transform (FFT) FFT is carried out, Obtain all doppler cells output signal D of l-th of range celll：
<mrow> <msub> <mi>D</mi> <mi>l</mi> </msub> <mo>=</mo> <msub> <mi>X</mi> <mi>l</mi> </msub> <msubsup> <mi>F</mi> <mi>D</mi> <mi>H</mi> </msubsup> <mo>=</mo> <mo>&amp;lsqb;</mo> <mtable> <mtr> <mtd> <msub> <mi>D</mi> <mrow> <mi>l</mi> <mo>_</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>D</mi> <mrow> <mi>l</mi> <mo>_</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>D</mi> <mrow> <mi>l</mi> <mo>_</mo> <mi>K</mi> </mrow> </msub> </mtd> </mtr> </mtable> <mo>&amp;rsqb;</mo> </mrow>
Wherein, FDFor FFT matrix, subscript H represents transposition, Dl_iFor l-th of range cell, i-th of doppler cells output letter Number, 1≤i≤K；To XlReceived and wave beam Σ by digital beam froming DBF formationl, ΣlIt is expressed as：
Σl=wΣXl
Wherein, wΣFor reception and wave beam weight；
To reception and wave beam ΣlFFT is carried out, all doppler cells of l-th of range cell is obtained and receives and wave beam output letter Number DΣ,l：
<mrow> <msub> <mi>D</mi> <mrow> <mi>&amp;Sigma;</mi> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;Sigma;</mi> <mi>l</mi> </msub> <msubsup> <mi>F</mi> <mrow> <mi>&amp;Sigma;</mi> <mo>,</mo> <mi>D</mi> </mrow> <mi>H</mi> </msubsup> <mo>=</mo> <mo>&amp;lsqb;</mo> <mtable> <mtr> <mtd> <msub> <mi>D</mi> <mrow> <mi>&amp;Sigma;</mi> <mo>,</mo> <mi>l</mi> <mo>_</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>D</mi> <mrow> <mi>&amp;Sigma;</mi> <mo>,</mo> <mi>l</mi> <mo>_</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>D</mi> <mrow> <mi>&amp;Sigma;</mi> <mo>,</mo> <mi>l</mi> <mo>_</mo> <mi>K</mi> </mrow> </msub> </mtd> </mtr> </mtable> <mo>&amp;rsqb;</mo> </mrow>
Wherein, FΣ,DFor with wave beam FFT matrix, DΣ,l_iReceived and wave beam for l-th of range cell, i-th of doppler cells Output signal；For l-th of range cell, DΣ,lIn maximum absolute value value where doppler cells be designated as l_d_max, l_ D_max correspond to the Doppler frequency f of receiver main-lobe clutterd_l；
Step 2: main-lobe clutter spatial frequency is estimated：In l-th of range cell, to D in step onelIn be in l_d_max positions Doppler cells output signal namely maximum Doppler element output signal Dl_d_maxThe sparse reconstruct in spatial domain is carried out, then passes through weight Heart blending algorithm accurately calculates and obtains main-lobe clutter spatial frequency, the maximum Doppler element output signal in sparse reconstruct Dl_d_maxFor measurement vector；Specially：
2.1st, iterative parameter initial value is set：Residual vector initial value r0=Dl_d_max, degree of rarefication initial value S=1, iteration count Measure initial value t=1, supported collection initial value Ω0For empty setAllowable error is||·||2For L2Norm is transported Calculate；The clutter spatial domain amplitude distribution for obtaining sparse reconstruct by the t times iterative estimate isIts initial valueM is N × Ns The observing matrix of dimension, and M is one group of super complete base being made up of spatial domain steering vector, NsFor spatial domain quantifying unit number, μpRepresent to see Survey matrix M pth row, 1≤p≤Ns；
2.2nd, supported collection updates：According toFind out observing matrix M and residual vector rt-1It is related Property maximum rowAnd update supported collection
Wherein, |<rt-1,μp>| to seek rt-1And μpThe absolute value of inner product,It is right when taking maximum for function f (x) The variable x answered value,It is to ask in the t times iteration to make rt-1And μpThe absolute value of inner product is most Row when bigΩtThe supported collection obtained for the t times iteration, by preceding t-1 supported collection Ωt-1WithThe new set constituted,For Ωt-1And rowUnion；
2.3rd, sparse reconstruct updates：The clutter spatial domain amplitude obtained using the t times iterative estimate of least-squares estimation Algorithm for Solving Distribution
<mrow> <msub> <mover> <mi>&amp;sigma;</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <mi>arg</mi> <munder> <mi>min</mi> <msub> <mover> <mi>&amp;sigma;</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> </munder> <mo>|</mo> <mo>|</mo> <msub> <mi>D</mi> <mrow> <mi>l</mi> <mo>_</mo> <mi>d</mi> <mo>_</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>&amp;Omega;</mi> <mi>t</mi> </msub> <msub> <mover> <mi>&amp;sigma;</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> <mo>|</mo> <msub> <mo>|</mo> <mn>2</mn> </msub> </mrow>
Wherein,Corresponding variable x value when taking minimum value for function f (x), It is to ask in the t times iterationWhen taking minimum valueValue；
2.4th, residual vector updates：
2.5th, iteration is adjudicated：By residual vector rtWith allowable error δlMake decisions, if | | rt||2＞ δl, then estimation is iterated, T=t+1, linearly increasing degree of rarefication S value are S=S+1, repeat step 2.2 to step 2.4；If | | rt||2≤δl, then stop changing In generation, sparse reconstruct is completed, perform step 2.6；
2.6th, in last time iterative estimate, terminate to remember during sparse reconstructσl_d_maxAs l-th range cell Maximum Doppler element output signal Dl_d_maxThe spatial domain amplitude distribution of the improved sparse reconstruct of OMP algorithms；
L-th of range cell main-lobe clutter spatial frequency f is obtained using center of gravity blending algorithms_l：
<mrow> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&amp;sigma;</mi> <mrow> <mi>l</mi> <mo>_</mo> <mi>d</mi> <mo>_</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mi>q</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>q</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>/</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&amp;sigma;</mi> <mrow> <mi>l</mi> <mo>_</mo> <mi>d</mi> <mo>_</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mi>q</mi> </mrow> </msub> </mrow>
Wherein, NqObtained σ is reconstructed to be sparsel_d_maxMain-lobe clutter scattering unit number, fs,qFor σl_d_maxQ-th of main-lobe clutter The spatial frequency of scattering unit, σl_d_max,qFor σl_d_maxQ-th of main-lobe clutter scattering unit range value；
Step 3: main-lobe clutter adaptive equalization, is specially：
The spectral centroid of the main-lobe clutter of l-th of the range cell obtained by step one and step 2 is (fs_l,fd_l), if detection The spectral centroid of range cell main-lobe clutter is (fs_0,fd_0), fs_0And fd_0The respectively detecting distance unit main-lobe clutter The difference Δ f of spatial frequency and Doppler frequency, then l-th of range cell and the Doppler frequency of detecting distance unitd_l, space The difference Δ f of frequencys_lRespectively：
Δfd_l=fd_l-fd_0
Δfs_l=fs_l-fs_0
Therefore, the normalization Doppler frequency compensation factor T of l-th of range cellt_lWith spatial frequency compensating factor Ts_lRespectively For：
<mrow> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mi>j</mi> <mn>2</mn> <msub> <mi>&amp;pi;&amp;Delta;f</mi> <mrow> <mi>d</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>/</mo> <msub> <mi>f</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mi>j</mi> <mn>2</mn> <mi>&amp;pi;</mi> <mo>(</mo> <mrow> <mi>K</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>d</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>/</mo> <msub> <mi>f</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mi>j</mi> <mn>2</mn> <msub> <mi>&amp;pi;&amp;Delta;f</mi> <mrow> <mi>s</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mi>j</mi> <mn>2</mn> <mi>&amp;pi;</mi> <mo>(</mo> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>s</mi> <mo>_</mo> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
Wherein, frFor pulse recurrence frequency, j is imaginary unit；
The data of l-th of range cell are after adaptive equalization：
Cl=Ts_lXlTt_l。
2. a kind of bistatic airborne radar self-adapting clutter based on improvement OMP according to claim 1 composes compensation method, its It is characterised by, XlIt is expressed as follows：
Wherein, Sl_iSignal phasor, 1≤i≤K are received for each array element of l-th of range cell, i-th of pulse.
3. a kind of bistatic airborne radar self-adapting clutter based on improvement OMP according to claim 1 composes compensation method, its It is characterised by, N=16.
4. a kind of bistatic airborne radar self-adapting clutter based on improvement OMP according to claim 1 composes compensation method, its It is characterised by, K=64.
5. a kind of bistatic airborne radar self-adapting clutter based on improvement OMP according to claim 1 composes compensation method, its It is characterised by, Ns=128.
CN201710427524.2A 2017-06-08 2017-06-08 It is a kind of that compensation method is composed based on the bistatic airborne radar self-adapting clutter for improving OMP Pending CN107329117A (en)

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