CN106896350A - Clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS GMTI patterns - Google Patents

Clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS GMTI patterns Download PDF

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CN106896350A
CN106896350A CN201710147694.5A CN201710147694A CN106896350A CN 106896350 A CN106896350 A CN 106896350A CN 201710147694 A CN201710147694 A CN 201710147694A CN 106896350 A CN106896350 A CN 106896350A
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target
clutter
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闫贺
***
朱岱寅
李勇
张劲东
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Nanjing University of Aeronautics and Astronautics
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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
    • G01S7/414—Discriminating targets with respect to background clutter

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

Abstract

The invention discloses clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS GMTI patterns, belong to technical field of radar target acquisition.The characteristics of present invention is constituted according to wide area monitoring mode echo, with reference to Relax algorithms, devises the alternative manner for carrying out clutter recognition.Then propose on the basis of clutter recognition, iteration is proceeded for the range-doppler cells that there is moving-target, be capable of achieving the accurate estimation of moving-target parameter.

Description

Clutter recognition and parameter Estimation based on Relax algorithms under a kind of WAS-GMTI patterns Method
Technical field
The present invention relates to clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS-GMTI patterns, category In technical field of radar target acquisition.
Background technology
Wide area monitoring mode, along quick, the periodic scan in flight path direction, is capable of achieving large area region monitoring by wave beam. It is higher that the pattern revisits rate, can from different perspectives find moving-target, and then improve the detection probability of moving-target.Further, since Can not in the same time Multiple-Scan to moving-target so that possibility is described as being to moving-target track, be capable of achieving moving-target with Track.The mode of operation can quickly provide the moving-target information being present in broader region, therefore have on civilian and military Huge application value, it has also become one of indispensable mode of operation of airborne reconnais sance radar.
Moving-target is extracted from the echo of wide area monitoring mode to be needed to carry out effective suppression to the clutter composition in echo. Foreign countries with wide area function for monitoring radar system, its Clutter suppression algorithm mostly all in confidential state, it is known that have the U.S. JSTARS systems use clutter recognition Non-Interference Algorithm (CSI) and Germany PAMIR systems use Scan-MTI calculation Method.CSI algorithms are substantially that a kind of passage offsets method, and its clutter suppression capability is limited, and in cancellation process, moving-target Energy can also be subject to different degrees of offseting (depend on radial velocity and azimuth of moving-target etc.).Scan-MTI algorithm sheets It is the 1-DT algorithms in dimensionality reduction STAP in matter, its clutter recognition performance can be greatly reduced under non-homogeneous clutter environment.This be because For STAP algorithms need to estimate the clutter plus noise covariance square of this unit by the information of adjacent unit in actual applications Battle array, under non-homogeneous clutter environment often there is larger error in the covariance matrix of this estimation, so as to cause clutter to be filtered The hydraulic performance decline of ripple device.
The content of the invention
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention to provide base under a kind of WAS-GMTI patterns In the clutter recognition and method for parameter estimation of Relax algorithms, the method need not estimate clutter plus noise covariance matrix, non- There is preferably clutter recognition effect under uniform environment.Simultaneously on the basis of clutter recognition, to detect the distance of moving-target- Doppler cells are continuing with Relax algorithms and are iterated treatment, can also obtain the accurate estimation of moving-target orientation angles.It is logical The result for crossing emulation data demonstrates the validity and feasibility of the method.
Technical scheme：To achieve the above object, the technical solution adopted by the present invention is：
Clutter recognition and method for parameter estimation based on Relax algorithms, comprise the following steps under a kind of WAS-GMTI patterns：
Step 1, data prediction：The echo that wide area monitoring mode is received is entered into row distance to pulse pressure, orientation FFT respectively Conversion, obtains the data in distance-Doppler domain.
Step 2, the clutter recognition based on Relax algorithms：Using the Clutter suppression algorithm based on Relax algorithms to step 1 Each distance-Doppler unit in the data in the distance-Doppler domain for obtaining carries out clutter recognition, after obtaining clutter recognition The moving-target component of estimation.
Step 3, CFAR detection：The moving-target component of the estimation after the clutter recognition obtained to step 2 carries out unit and puts down Equal CFAR detection, the distance-Doppler element number of the moving-target that acquisition is detected.
Step 4, based on the parameter Estimation of Relax algorithms, the distance-Doppler unit of the moving-target to detecting is used Relax algorithms proceed iteration to realize the accurate estimation of moving-target orientation angle of squint, until the orientation angle of squint of moving-target Keep constant rear termination iteration.Then the doppler cells for being fallen into according to the moving-target orientation angle of squint and moving-target estimated, meter The radial velocity of moving-target is calculated, moving-target parameter Estimation is realized.
Preferably：The Clutter suppression algorithm based on Relax algorithms for distance-Doppler domain each distance-it is many General Le unit, completes to travel through, one of all doppler cells of for searching loops using two nesting for circulations, another The all range cells of for searching loops, the nesting order of the two for circulations can be selected arbitrarily.
The distance-Doppler domain obtained to step 1 using the Clutter suppression algorithm based on Relax algorithms in the step 2 Data in each distance-Doppler unit carry out the method for clutter recognition and comprise the following steps：
Step 2-1, initialization, initialization completes following computing：
Wherein, Ce(m, n) represents the clutter component estimated, m represents the sequence number of doppler cells, and n represents range cell Sequence number, M represents the number of doppler cells, and N represents the number of many range cells, ac(θc(m)) represent m-th doppler cells The steering vector of correspondence clutter,
dk(k=1,2,3...K) the distance between k-th passage and reference channel (d is represented1=0), K represents receiving channel Number, θcThe corresponding orientation angles of m-th doppler cells of (m) expression, Z (m, n) m-th of expression, n-th of doppler cells The corresponding echo vector of range cell, H represents conjugate transposition operation；
Step 2-2, moving-target information estimating part：It is sequentially completed following computings：
Zb(m, n)=Z (m, n)-ac(θc(m))Ce(m,n)
θte(m, n)=arg max
Wherein, Zb(m, n) represents the residual volume for rejecting clutter component back echo, θte(m, n) represents what is obtained by search Moving-target azimuth, θsFor the electricity of wave beam sweeps angle, θ3dBIt is the 3dB main lobe widths at respective scanned angle, θt(m, n) is represented more than m-th The general orientation angle of squint for strangling moving-target in n-th range cell of unit, Te(m, n) represents the moving-target component estimated,WithClutter component and dynamic mesh are represented respectively The spatial domain steering vector of component is marked,
Step 2-3, clutter information estimating part：It is sequentially completed following computings：
Zc(m, n)=Z (m, n)-at(θte(m,n))Te(m,n)
Wherein, Zc(m, n) represents the residual volume for rejecting moving-target component back echo, at(θte(m, n)) represent that estimates moves Target spatial domain steering vector.
Preferably：After step 2-3 is completed, again returning to step 2-2 carries out next iteration.It is generally completed 1-2 times repeatedly Dai Hou, clutter can be obtained by effective suppression.
Beneficial effect：The present invention compared to existing technology, has the advantages that：
The characteristics of present invention is constituted according to wide area monitoring mode echo, with reference to Relax algorithms, devising carries out clutter recognition Alternative manner.Then propose on the basis of clutter recognition, proceed for the distance-Doppler unit that there is moving-target Iteration, is capable of achieving the accurate estimation of moving-target parameter, and the algorithm can make full use of existing channel number, and need not estimate Clutter plus noise covariance matrix, has preferably clutter recognition effect under non-homogeneous environment.Simultaneously in the base of clutter recognition On plinth, the distance-Doppler unit to detecting moving-target is continuing with Relax algorithms and is iterated treatment, can obtain dynamic The accurate estimation of target bearing angle.Therefore can effectively be applied in systems in practice.
Brief description of the drawings
Fig. 1 is wide area monitoring mode space geometry graph of a relation.
Fig. 2 is clutter recognition and parameter Estimation flow chart based on Relax algorithms.
Fig. 3 is the echo information of simulating scenes.The CNR distribution maps of Fig. 3 (a) simulating scenes, Fig. 3 (b) echo amplitude normalizings Change distribution map.
Clutter recognition (amplitude normalization) and CFAR testing result figures when Fig. 4 is in the absence of channel error.Fig. 4 (a) is empty When 1DT-SAP algorithm clutter recognition result figures, Fig. 4 (b) Relax algorithm first time iteration result figures, Fig. 4 (c) Relax algorithms Second iteration result figure, the CA-CFAR testing result figures after Fig. 4 (d) Relax algorithm clutter recognitions.
Fig. 5 is clutter recognition (amplitude normalization) and the CFAR testing result figures when there is channel error.Fig. 5 (a) space-times 1DT-SAP algorithm clutter recognition result figures, Fig. 5 (b) Relax algorithm first time iteration result figures, Fig. 5 (c) Relax algorithms second Secondary iteration result figure, the CA-CFAR testing result figures after Fig. 5 (d) Relax algorithm clutter recognitions.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is furture elucidated, it should be understood that these examples are merely to illustrate this Invention rather than limitation the scope of the present invention, after the present invention has been read, those skilled in the art are to of the invention various The modification of the equivalent form of value falls within the application appended claims limited range.
Clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS-GMTI (wide area monitoring) pattern, such as Fig. 1, shown in 2, comprise the following steps：
Step 1, data prediction：The echo that wide area monitoring mode is received is entered into row distance to pulse pressure, orientation FFT respectively Conversion, that is, transform data to distance-Doppler domain and wait further treatment, obtains the data in distance-Doppler domain.
Step 2, the clutter recognition based on Relax algorithms：Using the Clutter suppression algorithm based on Relax algorithms to step 1 Each distance-Doppler unit in the data in the distance-Doppler domain for obtaining carries out clutter recognition, after obtaining clutter recognition The moving-target component of estimation.
Each the distance-Doppler list of the Clutter suppression algorithm based on Relax algorithms for distance-Doppler domain Unit, traversal is completed using two nesting for circulations, and one of all doppler cells of for searching loops, another for is followed Ring travels through all range cells, and the nesting order of the two for circulations can be selected arbitrarily.
It is every in the data in the distance-Doppler domain obtained to step 1 using the Clutter suppression algorithm based on Relax algorithms The method that individual distance-Doppler unit carries out clutter recognition is comprised the following steps：
Step 2-1, initialization, initialization completes following computing：
Wherein, Ce(m, n) represents the clutter component estimated, m represents the sequence number of doppler cells, and n represents range cell Sequence number, M represents the number of doppler cells, and N represents the number of many range cells, ac(θc(m)) represent m-th doppler cells The steering vector of correspondence clutter,
dk(k=1,2,3...K) the distance between k-th passage and reference channel (d is represented1=0), K represents receiving channel Number, θcThe corresponding orientation angles of m-th doppler cells of (m) expression, Z (m, n) m-th of expression, n-th of doppler cells The corresponding echo vector of range cell, H represents conjugate transposition operation；
Step 2-2, moving-target information estimating part：It is sequentially completed following computings：
Zb(m, n)=Z (m, n)-ac(θc(m))Ce(m,n)
θte(m, n)=arg max
Wherein, Zb(m, n) represents the residual volume for rejecting clutter component back echo, θte(m, n) represents what is obtained by search Moving-target azimuth, θsFor the electricity of wave beam sweeps angle, θ3dBIt is the 3dB main lobe widths at respective scanned angle, θt(m, n) is represented more than m-th The general orientation angle of squint for strangling moving-target in n-th range cell of unit, Te(m, n) represents the moving-target component estimated,WithClutter component and dynamic mesh are represented respectively The spatial domain steering vector of component is marked,
Step 2-3, clutter information estimating part：It is sequentially completed following computings：
Zc(m, n)=Z (m, n)-at(θte(m,n))Te(m,n)
Wherein, Zc(m, n) represents the residual volume for rejecting moving-target component back echo, at(θte(m, n)) represent that estimates moves Target spatial domain steering vector.After step 2-3 is completed, again returning to step 2-2 carries out next iteration.It is generally completed 1-2 times After iteration, clutter can be obtained by effective suppression.Subsequently into step 3.
Step 3, CFAR detection：The moving-target component of the estimation after the clutter recognition obtained to step 2 carries out unit and puts down Equal CFAR detection, the distance-Doppler element number of the moving-target that acquisition is detected.
Step 4, based on the parameter Estimation of Relax algorithms, the distance-Doppler unit of the moving-target to detecting is used Relax algorithms proceed iteration to realize the accurate estimation of moving-target orientation angle of squint.Clutter is realized Relax iteration is carried out During suppression, there is the orientation angle of squint θ for moving-targetteThe step of being estimated.Changed in the Relax for carrying out clutter recognition During generation, the estimation to moving-target orientation angle of squint can be relatively coarse.If the distance-Doppler list to detecting moving-target Unit, iteration is proceeded using Relax algorithms, the accurate estimation of moving-target orientation angle of squint is capable of achieving, until the side of moving-target Position angle of squint keeps constant rear termination iteration.Then the Doppler for being fallen into according to the moving-target orientation angle of squint and moving-target estimated Unit, can calculate the radial velocity of moving-target, realize moving-target parameter Estimation.
It should be noted that during above-mentioned treatment, if certain distance-Doppler unit does not contain the letter of moving-target Breath, then it is considered that all physical quantitys related to moving-target are zero.
Clutter recognition and method for parameter estimation based on Relax algorithms under a kind of WAS-GMTI patterns are described above, this In verified and analyzed using the wide area monitoring mode echo data of emulation.The systematic parameter of emulation is as follows：Carrier frequency 9.45GHz, pulse recurrence frequency 6kHz, platform flying speed 100m/s, channel number is 5, the arteries and veins launched on each ripple position Number is rushed for 128.In order to simplify problem, here emulate beam position be 90 ° (positive side-looking) in the case of scene echo (hypothetically Face backscattering coefficient Gaussian distributed).It is how general at each using doppler cells and the one-to-one relationship of orientation angles Strangle and arrange 1 point target on the corresponding azimuth of unit, so arranging 128 points in each range cell.Meanwhile, The noise component(s) of Gaussian Profile is added in echo.300 range cells (1000-1299 range cells) are intercepted in emulation experiment Echo data be analyzed.In order to simulate the heterogeneity of clutter in actual scene, there is larger ripple in the CNR of simulating scenes It is dynamic, its along distance to change curve such as Fig. 3 (a) it is shown.In order to verify the stability of set calculating method, here respectively to emulation Echo and the echo that there is channel error in the absence of channel error processed.Wherein, there is passage during channel error Amplitude phase error distribution is as shown in table 2.In addition, 6 moving-targets of amplitude identical are added in scene echoes, by Range compress With the SNR ≈ 23dB after orientation FFT, the distribution of their radial velocity, residing range cell and orientation angle of squint is such as the institute of table 1 Show.
The moving-target information added in the echo of table 1.
Amplitude and the phase error distribution of the interchannel of table 2.
Clutter recognition and parameter estimation experiment based on Relax algorithms：
Step 1, i.e. data prediction are carried out first.Artificial echo is entered row distance respectively to pulse pressure, orientation FFT, i.e., Transform data to shown in the result such as Fig. 3 (b) behind distance-Doppler domain.
Then step 2 is carried out, i.e., clutter recognition is carried out using Relax algorithms.Fig. 4 (b) and Fig. 4 (c) are respectively Relax calculations Method carries out the clutter recognition result for the first time and after second iteration.For the ease of being compared with existing Clutter suppression algorithm, Fig. 4 (a) gives the result that clutter recognition is carried out using space-time 1DT-SAP algorithms (one kind of STAP algorithms).From Fig. 4 (a) As can be seen that being subject to the heteropical influence of clutter, the covariance matrix of clutter plus noise cannot effectively be estimated, so that Cause clutter recognition hydraulic performance decline.Greatly reduced compared to the clutter residue in Fig. 4 (a), Fig. 4 (b) and Fig. 4 (c), thus it is non-homogeneous The clutter recognition result based on Relax algorithms is better than space time processing result under environment.This will be attributed to based on Relax algorithms Clutter recognition process need not estimate clutter plus noise covariance matrix, thus will not be subject to the heteropical influence of clutter.Together When, comparison diagram 4 (b) and Fig. 4 (c) as can be seen that methods presented herein reached in first time iteration it is relatively good miscellaneous Ripple inhibition, second iteration is very small compared to the improvement of first time iteration, therefore in the processing procedure of real data, Clutter recognition process based on Relax algorithms generally carries out 1-2 iteration.
Step 3 carries out CA-CFAR treatment.CA-CFAR testing result figures such as Fig. 4 (d) institutes after Relax algorithm clutter recognitions Show, six in figure moving-target obtains effective detection.
Step 4 pair detects the distance-Doppler unit of moving-target, and iteration is proceeded using Relax algorithms, if through Keep constant after the orientation angle of squint of moving-target after iteration several times, then terminate iteration.According to the moving-target orientation stravismus estimated Angle, the doppler cells fallen into reference to moving-target can calculate the radial velocity of moving-target.Given in the emulation 1 of table 3 and adopted The result of moving-target parameter Estimation is carried out with Relax algorithms, the actual parameter error of the moving-target with being added in emulation is very small.
The moving-target parameter estimation result of table 3.
Above-mentioned simulation results show based on Relax algorithms carry out wide area monitoring mode clutter recognition and parameter Estimation can Row and validity.
Echo to there is channel error is processed, as a result as shown in the emulation 2 in Fig. 5 and Biao 3.It can be found that i.e. Make in the case where there is channel error, clutter recognition works well, and the result estimated using Relax algorithms still have compared with Precision high, simply iterations be higher than situation during in the absence of channel error.Carried out based on Relax algorithms so as to demonstrate The robustness of wide area monitoring mode clutter recognition and parameter Estimation.
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (4)

1. the clutter recognition and method for parameter estimation of Relax algorithms are based under a kind of WAS-GMTI patterns, it is characterised in that including Following steps：
Step 1, data prediction：The echo that wide area monitoring mode is received is entered into row distance respectively to become to pulse pressure, orientation FFT Change, obtain the data in distance-Doppler domain；
Step 2, the clutter recognition based on Relax algorithms：Step 1 is obtained using the Clutter suppression algorithm based on Relax algorithms Distance-Doppler domain data in each distance-Doppler unit carry out clutter recognition, obtain the estimation after clutter recognition Moving-target component；
Step 3, CFAR detection：It is permanent that the moving-target component of the estimation after the clutter recognition obtained to step 2 carries out cell-average False-alarm detection, the distance-Doppler element number of the moving-target that acquisition is detected；
Step 4, based on the parameter Estimation of Relax algorithms, the distance-Doppler unit of the moving-target to detecting uses Relax Algorithm proceeds iteration to realize the accurate estimation of moving-target orientation angle of squint, until the orientation angle of squint of moving-target keeps not Terminate iteration after change；Then the doppler cells for being fallen into according to the moving-target orientation angle of squint and moving-target estimated, calculate dynamic mesh Target radial velocity, realizes moving-target parameter Estimation.
2. the clutter recognition and method for parameter estimation of Relax algorithms are based under WAS-GMTI patterns according to claim 1, It is characterized in that：Each distance-Doppler of the Clutter suppression algorithm based on Relax algorithms for distance-Doppler domain Unit, traversal, one of all doppler cells of for searching loops, another for are completed using two nesting for circulations The all range cells of searching loop, the nesting order of the two for circulations can be selected arbitrarily.
3. the clutter recognition and method for parameter estimation of Relax algorithms are based under WAS-GMTI patterns according to claim 1, It is characterized in that：The distance-Doppler obtained to step 1 using the Clutter suppression algorithm based on Relax algorithms in the step 2 The method that each distance-Doppler unit in the data in domain carries out clutter recognition is comprised the following steps：
Step 2-1, initialization, initialization completes following computing：
C e ( m , n ) = a c H ( θ c ( m ) ) Z ( m , n ) M
Wherein, Ce(m, n) represents the clutter component estimated, m represents the sequence number of doppler cells, and n represents the sequence number of range cell, M The number of doppler cells is represented, N represents the number of many range cells, ac(θc(m)) represent that m-th doppler cells correspondence is miscellaneous The steering vector of ripple,
a c ( θ c ( m ) ) = 1 exp { - 2 π λ · d 2 · cosθ c ( m ) } ... exp { - 2 π λ · d K · cosθ c ( m ) } ,
dk(k=1,2,3...K) the distance between k-th passage and reference channel (d is represented1=0), K represents the individual of receiving channel Number, θcM the corresponding orientation angles of m-th doppler cells of () expression, Z (m, n) represents m-th doppler cells, n-th distance The corresponding echo vector of unit, H represents conjugate transposition operation；
Step 2-2, moving-target information estimating part：It is sequentially completed following computings：
Zb(m, n)=Z (m, n)-ac(θc(m))Ce(m,n)
θte(m, n)=arg max
θ s + θ 3 d B 2 θ t ( m , n ) = θ s - θ 3 d B 2 | a t H ( θ t ( m , n ) ) Z b ( m , n ) | 2
T e ( m , n ) = a t H ( θ t e ( m , n ) ) Z b ( m , n ) K
Wherein, Zb(m, n) represents the residual volume for rejecting clutter component back echo, θte(m, n) represents the dynamic mesh obtained by search Mark azimuth, θsFor the electricity of wave beam sweeps angle, θ3dBIt is the 3dB main lobe widths at respective scanned angle, θt(m, n) represents m-th Doppler The orientation angle of squint of moving-target, T in n-th range cell of unite(m, n) represents the moving-target component estimated,WithClutter component and dynamic mesh are represented respectively The spatial domain steering vector of component is marked,
Step 2-3, clutter information estimating part：It is sequentially completed following computings：
Zc(m, n)=Z (m, n)-at(θte(m,n))Te(m,n)
C e ( m , n ) = a c H ( θ c ( m ) ) Z c ( m , n ) K
Wherein, Zc(m, n) represents the residual volume for rejecting moving-target component back echo, at(θte(m, n)) represent that the moving-target estimated is empty Domain steering vector.
4. the clutter recognition and method for parameter estimation of Relax algorithms are based under WAS-GMTI patterns according to claim 3, It is characterized in that：After step 2-3 is completed, again returning to step 2-2 carries out next iteration；After being generally completed 1-2 iteration, Clutter can be obtained by effective suppression.
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