CN117269928A - Doppler oversampling projection clutter suppression method based on moving target detection radar - Google Patents
Doppler oversampling projection clutter suppression method based on moving target detection radar Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- 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
Abstract
The invention relates to a Doppler oversampling projection clutter suppression method based on a moving target detection radar, which comprises the following steps: calculating a slow time dimension guide vector matrix of clutter under the Doppler oversampling condition and a slow time dimension equivalent filter bank coefficient of a windowed MTD; calculating a windowed MTD Doppler guide vector matrix and carrying out singular value decomposition to obtain a projection matrix of clutter under the condition of Doppler oversampling; according to the projection matrix and the slow time dimension equivalent filter bank coefficient, an FIR filter bank coefficient is obtained through calculation, and filtering is carried out by using the FIR filter bank to obtain an MTD result after clutter suppression; if the amount of slow time sampling data is large, clutter suppression is performed after windowing FFT processing. The invention can reduce Doppler sidelobe in a non-clutter region while suppressing clutter, can use an FIR filter bank to realize less accumulated pulse number so as to reduce complexity, and can process more accumulated pulse number after FFT so as to reduce calculated amount.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a Doppler oversampling projection clutter suppression method based on a moving target detection radar.
Background
Radars generally work in complex environments, echoes of the radars generally contain signals reflected by targets and clutter generated by ground features or sea waves, and when the clutter power is too strong, target detection is seriously affected, so that targets are lost or false alarms are formed, and clutter suppression is an important link in radar signal processing.
The Moving Target Detection (MTD) technology is a clutter suppression technology which has been widely used in engineering practice, and the technology starts from a frequency domain, and filters in a slow time dimension by using a filter bank with center frequencies at different doppler frequency points so as to separate clutter and target signals in the frequency domain. From the filter bank design perspective, the MTD technique can be largely divided into the following three types: (1) A Moving Target Indication (MTI) filter is used to concatenate discrete time fourier transforms (DFTs). On one hand, the method can be realized through Fast Fourier Transform (FFT), so that the calculated amount is effectively reduced; on the other hand, the side lobes can be reduced through windowing, and the mutual interference among channels is reduced, so that clutter suppression and multi-target detection capability are improved. However, such methods suffer from a large loss of signal-to-noise ratio (SNR) for low-speed targets, and insufficient flexibility in clutter suppression regions. (2) adaptive clutter suppression based on covariance matrix. The method is used for estimating the covariance matrix of the clutter in the received signal, and optimizing the filter bank coefficient according to the covariance matrix, so that the clutter is restrained on the premise of ensuring the target SNR. The method has strong environmental adaptability, but needs to accurately estimate the clutter covariance matrix, has higher calculation complexity, and has poor stability related to matrix inversion operation, and can not reduce target side lobes by using a windowing mode. (3) filter bank design based on parameter convex optimization theory. The method solves the filter bank coefficient by constructing constraint conditions such as cost function, SNR loss, side lobe height and the like related to clutter power and using optimization theory such as convex optimization and the like. The method can inhibit clutter and simultaneously ensure indexes such as SNR loss, side lobe height and the like of each channel. However, the method is sensitive to parameters of cost functions and constraint conditions in the optimization problem, and the filter group coefficient obtained by optimization under the actual parameter condition may have the problem of insufficient clutter suppression capability.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a Doppler oversampling projection clutter suppression method based on a moving target detection radar, and solves the defects existing in the prior art.
The aim of the invention is achieved by the following technical scheme: a method for suppressing a doppler oversampled projection clutter based on a moving target detection radar, the method comprising:
calculating a slow time dimension steering vector matrix F of clutter under Doppler oversampling condition C And calculating a slow time dimension equivalent filter bank coefficient F of the windowed MTD;
calculating Doppler guiding vector matrix under the condition of Doppler oversampling in the windowed MTD result according to a Doppler guiding vector matrix formula, performing singular value decomposition, and combining a unit matrix to obtain a projection matrix of the clutter under the condition of Doppler oversampling；
Calculating the FIR filter group coefficient according to the projection matrix and the slow time dimension equivalent filter group coefficient FAnd according to->Filtering slow time sampled data y using FIR filter bankObtaining MTD result after clutter suppression>；
If the amount of slow time sampling data is large, clutter suppression is performed after windowing FFT processing.
The slow time dimension of the calculated clutter under the Doppler oversampling condition leads to a vector matrix F C And calculates the slow time-dimension equivalent filter bank coefficients F of the windowed MTD specifically including the following:
setting the number of MTD Doppler channels as D and the number of accumulated pulses as M according toCalculating to obtain a slow time dimension guide vector matrix F of clutter under the condition of Doppler oversampling C Wherein->，/>For clutter Doppler oversampling coefficient, < >>The number of single-side channels occupied by clutter Doppler is g, and m is the m accumulated pulse;
according toCalculating a slow time-dimension equivalent filter bank coefficient F of the windowed MTD, wherein +.>Is a column vector with all elements of 1, w is a doppler window coefficient,，/>， />and->Respectively representing Hadamard product and Kronecker product,>transpose is shown, d being the d-th MTD doppler channel.
Calculating a Doppler guiding vector matrix of the clutter in the windowed MTD result under the condition of Doppler oversampling according to a Doppler guiding vector matrix formula, performing singular value decomposition, and combining a unit matrix to obtain a projection matrix of the clutter under the condition of Doppler oversamplingThe method specifically comprises the following steps:
singular value decomposition is carried out on a Doppler guiding vector matrix G of clutter under the condition of Doppler oversampling to obtainWherein U is a left singular vector matrix of D× (2G+1), and +.>A singular value matrix of (2g+1) × (2g+1),a right singular vector matrix of (2g+1) × (2g+1);
setting the identity matrix as I according toObtaining projection matrix of clutter under Doppler oversampling condition。
Clutter suppression is carried out after the slow time is processed by adopting data y windowing FFT, and the clutter suppression specifically comprises the following steps:
by passing throughWindowing FFT processing is carried out on slow time sampling data y to obtain MTD result +.>；
According toPerforming clutter suppression, and calculating MTD result after clutter suppression>。
The invention has the following advantages: the Doppler oversampling projection clutter suppression method based on the moving target detection radar has the advantages that the coefficient solving mode is simple, the problem of poor matrix inversion stability is avoided, the depth and the width of clutter suppression notches can be adjusted by means of parameters alpha and G for different engineering applications, different clutter scenes can be dealt with, the method can be used under windowing conditions, doppler sidelobes in a non-clutter region can be reduced while clutter is suppressed, the complexity is reduced by using an FIR filter bank for fewer accumulated pulse numbers, and more accumulated pulse numbers can be processed after FFT so as to reduce the calculated amount.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 shows the amplitude response curves of all non-clutter channels, (a) the amplitude response curves of the MTI cascade FFT on all non-clutter channels, (b) the amplitude response curves of the covariance adaptive algorithm on all non-clutter channels, (c) the amplitude response curves of the filter coefficient optimization algorithm on all non-clutter channels, and (d) the amplitude response curves of the invention on all non-clutter channels;
FIG. 3 is a graph showing the amplitude response curve of the low channel with the number 4, (a) a graph showing the amplitude response curve of the MTI cascade FFT with the number 4, (b) a graph showing the amplitude response curve of the covariance adaptive algorithm with the low channel with the number 4, (c) a graph showing the amplitude response curve of the filter coefficient optimization algorithm with the low channel with the number 4, and (d) a graph showing the amplitude response curve of the low channel with the number 4;
FIG. 4 is a graph showing the amplitude response curve of the high channel with the number 50, (a) a graph showing the amplitude response curve of the MTI cascade FFT with the number 50, (b) a graph showing the amplitude response curve of the covariance adaptive algorithm with the high channel with the number 50, (c) a graph showing the amplitude response curve of the filter coefficient optimization algorithm with the high channel with the number 50, and (d) a graph showing the amplitude response curve of the high channel with the number 50;
fig. 5 is a schematic diagram of SNR loss curves of MTI cascaded FFT, covariance adaptive algorithm, filter coefficient optimization algorithm and the method of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of the present application, provided in connection with the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application. The invention is further described below with reference to the accompanying drawings.
The invention relates to a Doppler oversampling projection clutter suppression method based on a moving target detection radar, which calculates a clutter subspace under the condition of Doppler oversampling, projects the output of all channels of an MTD to a corresponding orthogonal subspace, and can effectively suppress clutter while maintaining the target sidelobe height and SNR performance; the method specifically comprises the following steps:
step 1: as shown in fig. 2, assuming that the MTD radar slow time sampling number m=128, the doppler channel number d=128, the clutter channel oversampling coefficient α=2, the clutter doppler occupies a single side channel number g=4, according to the following
；
Calculating to obtain slow time dimension guide vector matrix of clutter under Doppler oversampling condition. Wherein,。
step 2: calculation of
；
Wherein,. Setting an MTD Doppler window coefficient w to obtain a Chebyshev window with the side lobe height of-40 dB, and calculating
；
The slow time-dimension equivalent filter bank coefficient F of the windowed MTD is obtained. Wherein,column vector with all elements 1, +.>And->Respectively representing Hadamard product and Kronecker product,>representing the transpose.
Step 3: according to
；
And calculating to obtain a Doppler guiding vector matrix G of the clutter in the windowed MTD result under the condition of Doppler oversampling. Wherein,representing the conjugate transpose.
Step 4: singular value decomposition is carried out on a Doppler guiding vector matrix G of clutter under the condition of Doppler oversampling to obtain
；
Wherein the method comprises the steps ofThe size of (2) is 128×9,/v>Is 9 x 9,/o>The size of (2) is 9×9.
Step 5: according to
；
Obtaining projection matrix of clutter under Doppler oversampling condition. Wherein I is a 128×128 identity matrix.
Step 6: according to
；
Calculating to obtain FIR filter group coefficient F U 。
Step 7: according to
；
Filtering the slow time data y by using an FIR filter bank to obtain an MTD result after clutter suppression。
Considering that the number of slow time sampling points is 128, the calculation amount required by directly performing the FIR filtering is large, and the implementation manner of the FIR filter set in the original step 6 and the step 7 can be replaced by:
step 6: and (3) carrying out windowed FFT processing of a chebyshev window with the side lobe height of-40 dB on the slow time sampling data y:
；
obtaining MTD results。
Step 7: then the following clutter suppression is carried out on the basis:
；
thereby equivalently obtaining MTD results after clutter suppression with less calculation amount。
The following examines and compares the 4 MTD implementation methods of MTI cascaded FFT, covariance matrix-based adaptive clutter suppression filter bank, optimization theory-based filter bank, and filter bank generated by the method to illustrate the advantages of the method. As shown in fig. 2-4, the amplitude response curves of these 4 methods are given in turn for all non-clutter channels (removing zero channel and left and right 3 channels for 7 clutter channels), low doppler channel (4 th channel) and high doppler channel (50 th channel), respectively. Wherein, the MTI shown in (a) of figures 2-4 is a Chebyshev window with a cancellation pulse number of 3 and FFT plus-40 dB; the covariance matrix-based adaptive clutter suppression filter bank shown in (b) of fig. 2-4 has a relative clutter suppression width of 0.005; the filter bank based on the optimization theory shown in (c) of fig. 2-4 was obtained using a convex optimization tool box under the chebyshev window condition of-40 dB; fig. 2-4 (d) show the filter bank coefficients obtained in the example of the method of the present invention.
As shown in fig. 2, the above 4 methods all produce clutter suppression notches of a certain width. The problem that the optimization algorithm has unstable optimization results in partial channels is reflected by the fact that the notch depth of the optimization filter bank in fig. 2 (c) is low and the output amplitude is inconsistent.
FIG. 3 shows the low channel amplitude response of the 4 methods, wherein the side lobe heights of FIGS. 3 (a) and (b) are higher in the non-clutter suppressed region, affecting the multi-target detection effect; the clutter suppression capability of (c) is poor; (d) The method has good inhibition capability in the clutter suppression area, and has lower sidelobe height in the non-clutter suppression area, so that good target detection capability can be obtained.
FIG. 4 shows the high channel amplitude response of 4 methods, where clutter and sidelobe suppression effects of (a) are more desirable; the side lobe of (b) is higher; the clutter suppression capability of (c) is weak; (d) maintaining good clutter and sidelobe suppression capability.
Fig. 5 shows SNR loss curves for 4 methods, it can be seen that the SNR loss for each algorithm is mainly affected by windowing and channel crossing at high channels, with no windowing covariance adaptive filtering being essentially lossless at the channel center, but with greater channel crossing loss; the remaining 3 methods of adding the-40 dB chebyshev window are essentially identical in loss, varying between-1 dB and-3 dB. In the low channel, the MTI cascade FFT algorithm has larger loss, and the loss of the other 3 kinds is equivalent.
Therefore, the method has better clutter suppression capability and sidelobe suppression capability in each MTD channel, and has smaller SNR loss. The method of the invention can also use FFT to accelerate calculation, so the method has good engineering application value.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and adaptations, and of being modified within the scope of the inventive concept described herein, by the foregoing teachings or by the skilled person or knowledge of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (4)
1. The Doppler oversampling projection clutter suppression method based on the moving target detection radar is characterized by comprising the following steps of: the inhibition method comprises the following steps:
calculating a slow time dimension steering vector matrix F of clutter under Doppler oversampling condition C And calculating a slow time dimension equivalent filter bank coefficient F of the windowed MTD;
calculating Doppler guiding vector matrix under the condition of Doppler oversampling in the windowed MTD result according to a Doppler guiding vector matrix formula, performing singular value decomposition, and combining a unit matrix to obtain a projection matrix of the clutter under the condition of Doppler oversampling；
Calculating the FIR filter group coefficient according to the projection matrix and the slow time dimension equivalent filter group coefficient FAnd according to->Filtering the slow time sampling data y by using an FIR filter bank to obtain MTD result after clutter suppression>；
If the amount of slow time sampling data is large, clutter suppression is performed after windowing FFT processing.
2. The method for suppressing the doppler oversampled projection clutter based on the moving target detection radar according to claim 1, wherein: the slow time dimension of the calculated clutter under the Doppler oversampling condition leads to a vector matrix F C And calculates the slow time-dimension equivalent filter bank coefficients F of the windowed MTD specifically including the following:
setting the number of MTD Doppler channels as D and the number of accumulated pulses as M according toCalculating to obtain a slow time dimension guide vector matrix F of clutter under the condition of Doppler oversampling C Wherein->，/>For clutter Doppler oversampling coefficient, < >>The number of single-side channels occupied by clutter Doppler is g, and m is the m accumulated pulse;
according toThe slow time-dimension equivalent filter bank coefficients F of the windowed MTD are calculated, wherein,is a column vector with all elements of 1, w is a doppler window coefficient,，/>， />and->Respectively representing Hadamard product and Kronecker product,>transpose is shown, d being the d-th MTD doppler channel.
3. The method for suppressing the doppler oversampled projection clutter based on the moving target detection radar according to claim 1, wherein: calculating a Doppler guiding vector matrix of the clutter in the windowed MTD result under the condition of Doppler oversampling according to a Doppler guiding vector matrix formula, performing singular value decomposition, and combining a unit matrix to obtain a projection matrix of the clutter under the condition of Doppler oversamplingThe method specifically comprises the following steps:
singular value decomposition is carried out on a Doppler guiding vector matrix G of clutter under the condition of Doppler oversampling to obtainWherein U is a left singular vector matrix of D× (2G+1), and +.>A singular value matrix of (2g+1) × (2g+1),right singular vector matrix of (2g+1) × (2g+1), +.>Representing conjugate transposition, wherein D is the number of MTD Doppler channels, and G is the number of unilateral channels occupied by clutter Doppler;
setting the identity matrix as I according toObtaining projection matrix of clutter under Doppler oversampling condition +.>。
4. The method for suppressing the doppler oversampled projection clutter based on the moving target detection radar according to claim 1, wherein: clutter suppression is carried out after the slow time is processed by adopting data y windowing FFT, and the clutter suppression specifically comprises the following steps:
by passing throughWindowing FFT processing is carried out on slow time sampling data y to obtain MTD result +.>；
According toPerforming clutter suppression, and calculating MTD result after clutter suppression>。
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280571A1 (en) * | 2004-06-17 | 2005-12-22 | The Boeing Company | System, method and computer program product for detecting and tracking a moving ground target having a single phase center antenna |
| CN104007434A (en) * | 2014-05-29 | 2014-08-27 | 西安电子科技大学 | Radar moving target detection method based on sea clutter background of Doppler oversampling |
| CN106772303A (en) * | 2016-12-22 | 2017-05-31 | 西安电子工程研究所 | The channel level clutter suppression method of MTD radars |
| CN108375760A (en) * | 2018-01-29 | 2018-08-07 | 零八电子集团有限公司 | The FIR filter design method that radar clutter inhibits |
| CN108693509A (en) * | 2018-04-08 | 2018-10-23 | 中国人民解放军海军航空大学 | Frequency control battle array radar Ullage frequency focuses moving-target integration detection method |
| US20220404466A1 (en) * | 2021-02-26 | 2022-12-22 | University Of Kansas | Structure-based adaptive radar processing for joint interference cancellation and signal estimation |
-
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- 2023-11-21 CN CN202311553722.5A patent/CN117269928B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280571A1 (en) * | 2004-06-17 | 2005-12-22 | The Boeing Company | System, method and computer program product for detecting and tracking a moving ground target having a single phase center antenna |
| CN104007434A (en) * | 2014-05-29 | 2014-08-27 | 西安电子科技大学 | Radar moving target detection method based on sea clutter background of Doppler oversampling |
| CN106772303A (en) * | 2016-12-22 | 2017-05-31 | 西安电子工程研究所 | The channel level clutter suppression method of MTD radars |
| CN108375760A (en) * | 2018-01-29 | 2018-08-07 | 零八电子集团有限公司 | The FIR filter design method that radar clutter inhibits |
| CN108693509A (en) * | 2018-04-08 | 2018-10-23 | 中国人民解放军海军航空大学 | Frequency control battle array radar Ullage frequency focuses moving-target integration detection method |
| US20220404466A1 (en) * | 2021-02-26 | 2022-12-22 | University Of Kansas | Structure-based adaptive radar processing for joint interference cancellation and signal estimation |
Non-Patent Citations (1)
| Title |
|---|
| 陈帅: "海杂波背景下的过采样MTD方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, vol. 2016, no. 3, pages 136 - 2589 * |
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