CN116068500A - Novel space-time interference suppression method for multichannel synthetic aperture radar - Google Patents
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
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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
Abstract
The invention discloses a novel multichannel Synthetic Aperture Radar (SAR) space-time interference suppression method, which is based on a mathematical model of pitching dimension multichannel SAR, creatively combines a two-dimensional time domain low-rank recovery method with a airspace self-adaptive beam forming method, constructs a unified interference suppression optimization problem model, and provides an optimization problem closed solution based on an Alternating Direction Multiplier Method (ADMM) frame. Compared with the traditional interference suppression method, the SAR image result recovered by the interference suppression method has lower mean square error and higher structural similarity, namely the method has more effective interference suppression capability.
Description
Technical Field
The invention belongs to the technical field of SAR interference suppression, and particularly relates to a multichannel-based SAR space-time interference suppression method.
Background
Radar, as an important military monitoring and civil detection device, is increasingly important to human production and life along with the continuous development and popularization of electronic technology, from the earliest military specialization to weather detection and topography mapping to the current vehicle-mounted radar and intelligent home. Synthetic Aperture Radar (SAR) has made a leap type progress as an all-weather, all-day and high-resolution earth observation means, has unique advantages in application in the fields of disaster monitoring, environment monitoring, marine observation, resource exploration, disaster early warning, crop estimation, forest investigation, topography mapping, battlefield reconnaissance and the like, can play the roles of other earth observation means such as visible light, infrared and the like, and is increasingly valued by the world.
The synthetic aperture radar transmits and receives electromagnetic waves at a designed pulse repetition frequency, and performs imaging processing on echo data received at different positions within a period of time by means of the motion of the platform. The technology synthesizes a series of antenna apertures by means of a motion platform to form a very large antenna aperture, and essentially utilizes time resources and space resources to obtain high resolution. The development of the array signal processing technology brings possibility for realizing a high-resolution wide-amplitude SAR imaging system, and higher scanning breadth and better resolution can be realized through a multi-channel technology.
However, a major challenge faced by current SAR systems is that rf interference, whether unintentional interference of civilian equipment or intentional interference of military equipment, has an impact on SAR systems, and thus interference suppression techniques have received increased attention. The traditional SAR interference suppression technology mainly can be divided into a non-parametric method, a parametric method and a semi-parametric method, wherein the non-parametric method is easy to realize and has relatively low computational complexity, but the optimal performance cannot be obtained because part of useful information is ignored; the parameterization method considers the amplitude information and the phase information of the signals at the same time, so that the calculation complexity is high and the performance is excellent, but certain performance is still lost during processing; the semi-parameterization method converts the interference suppression problem into a convex optimization problem, protects the real echo while suppressing the interference, and can obtain very good performance despite the very high computational complexity.
Disclosure of Invention
The invention aims to solve the problem that the current high-resolution wide-amplitude SAR imaging system is easily affected by strong radio frequency interference, and the interference suppression problem of a multichannel SAR system is solved by using the method provided by the invention. The method and the device can adaptively process the original data of the SAR system and effectively inhibit interference signals.
In order to solve the problems, the specific technical scheme of the invention is as follows: a novel multichannel synthetic aperture radar space-time interference suppression method, comprising the steps of:
step 1: inputting a multichannel SAR echo signal with interference:
step 2: constructing a space-time interference suppression convex optimization problem model;
step 3: optimizing variables in turn by using an ADMM frame;
step 4: judging whether the convergence condition is met or the maximum iteration number is reached, if yes, carrying out the step 5, and if not, repeating the step 3;
step 5: and obtaining SAR echo signals after interference suppression for subsequent imaging.
The method can adaptively inhibit interference signals from original SAR signals so as to recover real SAR imaging results, and comprises the following specific implementation steps:
Step 2, combining a two-dimensional low-rank recovery method with a airspace self-adaptive beam forming method according to actual environment and SAR system parameters, introducing auxiliary variables at the same time, and constructing an interference suppression problem as a unified optimization problem, wherein the form is shown as follows
Wherein:
representing the original echo signal matrix of the input, +.>
Representing a real echo signal matrix without interference,
representing an interference signal matrix>
For the introduced auxiliary variable matrix, +.>
An operation of stacking vectors into a matrix is shown,
weighting vectors for arrays>
Indicating beam pointing +.>
Array steering vector of angle, +.>
In order to constrain the weights of the terms,
、/>
、/>
respectively representing the computing kernel norms,/->
Norms +.>
Manipulation of norms, ++>
As an arbitrarily small positive real number, superscript ++in the formula>
Representing the conjugate transpose operation. The optimization objective of this optimization problem is to minimize the regularized interference signal power, minimize the error of the echo signal, and minimize the variance of the results of the array beamforming. While meeting the objective, it is also necessary to constrain the error of the sum of the interfering signal component and the echo signal component to be as small as possible with the original received signal and to ensure that the overall variance is as small as possible with the beamforming gain in the desired signal direction being a constant value. When the error of each component is optimized, each channel is processed independently, and when the beam forming problem is optimized, the data of all channels is required to be stacked and converted into a matrix form of a two-dimensional array signal through a certain operation, and the matrix form is processed. Compared with the prior art, the method has the advantages that the anti-interference performance is improved by combining the space domain beam forming constraint condition, a more effective and accurate optimization problem model is constructed, and meanwhile, the matrix data is beneficial to efficient calculation and solving.
The step 3 is specifically as follows: in order to solve the optimization problem in step 2, each variable in the optimization problem is updated in turn by using an alternate direction multiplier (ADMM) framework, the original problem is decomposed into a low rank recovery problem and an adaptive beam forming problem, and then the low rank recovery problem is converted into an augmented lagrangian function form.
And step 3, dividing the optimization problem in the step 2 into two sub-problems of time domain RPCA interference suppression optimization problem and space domain self-adaptive beam forming, constructing an augmented Lagrangian function, and introducing Lagrangian multipliers and punishment super-parameters. The augmented Lagrangian equation in step 3 is
Wherein->
The function is a lagrange multiplier,
and 5, setting punishment super parameters.
And 4, optimizing the augmented Lagrangian function in the step 3 by using an ADMM frame, optimizing an interference signal matrix by using a singular value threshold method, optimizing an introduced auxiliary variable matrix by using a Lagrangian multiplier method, and optimizing a real echo signal matrix by using a soft threshold method. After that, the weight vector of the array antenna is optimized by using a linear constraint least squares (LCMV) method, and finally the remaining lagrangian multiplier and penalty parameters are optimized. And repeatedly cycling the variable optimization process until the convergence condition is met or the maximum iteration number is reached.
And 5, SAR imaging is carried out by using the real echo signals obtained in the step 4 after interference suppression and self-adaptive beam forming, so as to obtain a final output result, wherein the imaging process is similar to a coherent accumulation focusing process carried out by a conventional SAR system.
Further, the optimization problem solving sequence of the variables in the step 4 is that firstly, an interference matrix is optimized, then an auxiliary variable matrix is optimized, then a signal matrix and an array channel weighting vector are optimized, and finally, lagrangian multipliers and punishment super-parameters introduced in an augmented Lagrangian equation are optimized. And after each iteration solution is calculated through the ADMM framework, substituting the result of the iteration into the calculation formula of the next iteration for updating the variable. The singular value threshold method relaxes the non-convex optimization problem into a convex optimization problem, and the problem can be obtained by approximating a kernel norm and an F norm. The soft threshold method makes the multivariable optimization problem differentiable by constructing the monotonic function, so that the solution is convenient. The LCMV method is a traditional adaptive filtering algorithm for array signals, and reduces overall power anti-interference by limiting the gain in the desired direction to be constant.
Further, solving the variables in step 4
The iterative closed-form solution of the optimization problem of (2) is:
Further, solving the variables in step 4
An iterative closed-form solution to the optimization problem of +.>
A soft threshold function is used to assist the solution, wherein +.>
Representing the actual signal matrix estimation result of the next iteration,
the loss function representing the current iteration calculation, the superscript of all parameters representing the iteration round, ST representing the soft threshold function +.>
Wherein->
Representing an input matrix +.>
Representing the threshold used by the soft threshold function.
The novel space-time interference suppression method for the multichannel synthetic aperture radar has the following advantages:
1. the invention combines a low rank recovery method with an adaptive beam forming method;
2. the invention constructs a new space-time combined interference suppression optimization problem model;
3. the invention utilizes the ADMM framework to gradually solve the multivariable optimization problem;
4. the invention can adaptively process the input SAR original signal, output the signal after interference suppression, and effectively realize the suppression of narrowband and broadband radio frequency interference;
5. the method uses the assistance of the singular value threshold function and the soft threshold function when solving the optimization problem, improves the estimation accuracy and the anti-interference capability, reduces the root mean square error after interference suppression, and improves the structural similarity after interference suppression;
6. compared with the current method with better anti-interference capability, the method has the advantages that the performance is improved, the calculation complexity is reduced to a certain extent, and the calculation time is shortened.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2-1 is a multi-channel SAR mathematical model (overall) of the present invention;
2-2 a multi-channel SAR mathematical model (cross-sectional view) of the present invention;
FIGS. 2-3 are multiple channel SAR mathematical models (array antennas) of the present invention;
FIG. 3 is a SAR image of a true non-interfering signal used in the present invention;
FIG. 4 is a SAR image of a real disturbance signal used in the present invention;
FIG. 5 is an SAR image after anti-interference using LCMV method;
FIG. 6 is an SAR image after anti-interference using ESP method;
fig. 7 is an SAR image after interference rejection using the method of the present invention.
Detailed Description
The present invention is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the invention and not limiting of its scope, and various equivalent modifications to the invention will fall within the scope of the appended claims to the skilled person after reading the invention.
Example 1: a novel multichannel synthetic aperture radar space-time interference suppression method, comprising the steps of:
step 1: inputting a multichannel SAR echo signal with interference:
step 2: constructing a space-time interference suppression convex optimization problem model;
step 3: optimizing variables in turn by using an ADMM frame;
step 4: judging whether the convergence condition is met or the maximum iteration number is reached, if yes, carrying out the step 5, and if not, repeating the step 3;
step 5: and obtaining SAR echo signals after interference suppression for subsequent imaging.
The method can adaptively inhibit interference signals from original SAR signals so as to recover real SAR imaging results, and comprises the following specific implementation steps:
Step 2, combining a two-dimensional low-rank recovery method with a airspace self-adaptive beam forming method according to actual environment and SAR system parameters, introducing auxiliary variables at the same time, and constructing an interference suppression problem as a unified optimization problem, wherein the form is shown as follows
Wherein:
representing the original echo signal matrix of the input, +.>
Representing a real echo signal matrix without interference,
representing an interference signal matrix>
For the introduced auxiliary variable matrix, +.>
An operation of stacking vectors into a matrix is shown,
weighting vectors for arrays>
Indicating beam pointing +.>
Array steering vector of angle, +.>
In order to constrain the weights of the terms,
、/>
、/>
respectively representing the computing kernel norms,/->
Norms +.>
Manipulation of norms, ++>
As an arbitrarily small positive real number, superscript ++in the formula>
Representing the conjugate transpose operation. The optimization objective of this optimization problem is to minimize the regularized interference signal power, minimize the error of the echo signal, and minimize the variance of the results of the array beamforming. While meeting the objective, it is also necessary to constrain the error of the sum of the interfering signal component and the echo signal component to be as small as possible with the original received signal and to ensure that the overall variance is as small as possible with the beamforming gain in the desired signal direction being a constant value. When the error of each component is optimized, each channel is processed independently, and when the beam forming problem is optimized, the data of all channels is required to be stacked and converted into a matrix form of a two-dimensional array signal through a certain operation, and the matrix form is processed. Compared with the prior art, the method has the advantages that the anti-interference performance is improved by combining the space domain beam forming constraint condition, a more effective and accurate optimization problem model is constructed, and meanwhile, the matrix data is beneficial to efficient calculation and solving.
The step 3 is specifically as follows: in order to solve the optimization problem in step 2, each variable in the optimization problem is updated in turn by using an alternate direction multiplier (ADMM) framework, the original problem is decomposed into a low rank recovery problem and an adaptive beam forming problem, and then the low rank recovery problem is converted into an augmented lagrangian function form.
And step 3, dividing the optimization problem in the step 2 into two sub-problems of time domain RPCA interference suppression optimization problem and space domain self-adaptive beam forming, constructing an augmented Lagrangian function, and introducing Lagrangian multipliers and punishment super-parameters. The augmented Lagrangian equation in step 3 is
Wherein->
The function is a lagrange multiplier,
and 5, setting punishment super parameters.
And 4, optimizing the augmented Lagrangian function in the step 3 by using an ADMM frame, optimizing an interference signal matrix by using a singular value threshold method, optimizing an introduced auxiliary variable matrix by using a Lagrangian multiplier method, and optimizing a real echo signal matrix by using a soft threshold method. After that, the weight vector of the array antenna is optimized by using a linear constraint least squares (LCMV) method, and finally the remaining lagrangian multiplier and penalty parameters are optimized. And repeatedly cycling the variable optimization process until the convergence condition is met or the maximum iteration number is reached.
And 5, SAR imaging is carried out by using the real echo signals obtained in the step 4 after interference suppression and self-adaptive beam forming, so as to obtain a final output result, wherein the imaging process is similar to a coherent accumulation focusing process carried out by a conventional SAR system.
Further, the optimization problem solving sequence of the variables in the step 4 is that firstly, an interference matrix is optimized, then an auxiliary variable matrix is optimized, then a signal matrix and an array channel weighting vector are optimized, and finally, lagrangian multipliers and punishment super-parameters introduced in an augmented Lagrangian equation are optimized. And after each iteration solution is calculated through the ADMM framework, substituting the result of the iteration into the calculation formula of the next iteration for updating the variable. The singular value threshold method relaxes the non-convex optimization problem into a convex optimization problem, and the problem can be obtained by approximating a kernel norm and an F norm. The soft threshold method makes the multivariable optimization problem differentiable by constructing the monotonic function, so that the solution is convenient. The LCMV method is a traditional adaptive filtering algorithm for array signals, and reduces overall power anti-interference by limiting the gain in the desired direction to be constant.
Further, solving the variables in step 4
The iterative closed-form solution of the optimization problem of (2) is:
Further, solving the variables in step 4
An iterative closed-form solution to the optimization problem of +.>
A soft threshold function is used to assist the solution, wherein +.>
Representing the actual signal matrix estimation result of the next iteration,
the loss function representing the current iteration calculation, the superscript of all parameters representing the iteration round, ST representing the soft threshold function +.>
Wherein->
Representing an input matrix +.>
Representing the threshold used by the soft threshold function.
Example 2: the invention discloses a novel multichannel synthetic aperture radar space-time interference suppression method, which is implemented by selecting simulated SAR image data and real SAR image data. As shown in fig. 1, the specific implementation method of the present invention is as follows:
step 1: and reading the SAR image data with real original interference by using matlab software, wherein a large amount of suppression interference exists on the original data image.
Step 2: according to the actual environment and SAR system parameters, constructing a pitching multichannel SAR system model shown in figures 2-1, 2-2 and 2-3, combining a two-dimensional low rank recovery method with a airspace self-adaptive beam forming method, introducing auxiliary variables, and constructing an interference suppression problem as a unified optimization problem, wherein the mode is as follows
Step 3: dividing the optimization problem in the step 2 into two sub-problems of time domain RPCA interference suppression optimization problem and space domain self-adaptive beam forming, constructing an augmented Lagrangian function, and introducing Lagrangian multipliers and punishment super-parameters. Its augmented Lagrangian equation can be written as
Wherein->
Is Lagrangian multiplier, +.>
And 5, setting punishment super parameters.
Step 4: and (3) optimizing the augmented Lagrangian function in the step (3) by using an ADMM (adaptive modulation) framework through a programming program by using matlab software, optimizing an interference signal matrix by using a singular value threshold method, optimizing an introduced auxiliary variable matrix by using a Lagrangian multiplier method, and optimizing a real echo signal matrix by using a soft threshold method. After that, the weight vector of the array antenna is optimized by using a linear constraint least squares (LCMV) method, and finally the remaining lagrangian multiplier and penalty parameters are optimized. And repeatedly cycling the variable optimization process until the convergence condition is met or the maximum iteration number is reached. Further, the optimization problem solving sequence of the variables in the step 4 is that firstly, an interference matrix is optimized, then an auxiliary variable matrix is optimized, then a signal matrix and an array channel weighting vector are optimized, and finally, lagrangian multipliers and punishment super-parameters introduced in an augmented Lagrangian equation are optimized. And after each iteration solution is calculated through the ADMM framework, substituting the result of the iteration into the calculation formula of the next iteration for updating the variable. The singular value threshold method relaxes the non-convex optimization problem into a convex optimization problem, and the problem can be obtained by approximating a kernel norm and an F norm. The soft threshold method makes the multivariable optimization problem differentiable by constructing the monotonic function, so that the solution is convenient. The LCMV method is a traditional adaptive filtering algorithm for array signals, and reduces overall power anti-interference by limiting the gain in the desired direction to be constant.
Step 5: through the matlab program written in the step 4, echo signals of all channels after interference suppression and weighting coefficients corresponding to all channels, which are output by the method, can be obtained, and the final real echo signals for interference suppression can be obtained for imaging through weighted summation. The multichannel data are synthesized into single-channel data after being weighted by wave beam formation, and then the radar data are focused by using a conventional SAR coherent accumulation mode, so that the imaging process is completed.
In summary, in the novel multi-channel synthetic aperture radar space-time interference suppression method, imaging tests are performed on two simulated and real SAR images, the original SAR image is shown in fig. 3, and a large amount of interference exists on the interfered SAR image, as shown in fig. 4. The image processed by the method of the invention is shown in fig. 7, and the original signal is processed by the method, so that the effect of suppressing interference in the SAR system is finally realized. Fig. 5 and 6 are effects achieved by other methods, and are used for comparison with the method proposed by the present invention. The imaging effect of the method of the invention is relatively poor, and the imaging effect of the method of the invention is relatively good, so that the geomorphic features can be clearly displayed. In terms of data, the root mean square error of fig. 5 and 6 using other methods is higher than that of fig. 7 using the method of the present invention, and the structural similarity is lower than that of the method of the present invention.
The foregoing is only a preferred embodiment of the invention, it being noted that: while the invention has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (10)
1. A novel multichannel synthetic aperture radar space-time interference suppression method, which is characterized by comprising the following steps:
step 1: a multi-channel SAR echo signal with interference is input,
step 2: constructing a space-time interference suppression convex optimization problem model,
step 3: the variables are optimized in turn by using an alternate direction multiplier method framework,
step 4: judging whether the convergence condition is met or the maximum iteration number is reached, if yes, carrying out step 5, if no, repeating step 3,
step 5: and obtaining SAR echo signals after interference suppression for subsequent imaging.
2. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 1, wherein,
the step 1 is specifically as follows: reading an echo signal of a ground scanning imaging by an SAR system with pitching dimension multichannel beam forming capability, wherein the transmitting signal is linear frequency modulation continuous wave, and strong radio frequency interference exists in the echo signal;
the step 2 is specifically as follows: according to the actual environment and SAR system parameters, combining a two-dimensional low-rank recovery method with a airspace self-adaptive beam forming method, introducing auxiliary variables, and constructing the interference suppression problem as a unified optimization problem.
3. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 2, wherein,
the step 3 is specifically as follows: in order to solve the optimization problem in the step 2, each variable in the optimization problem is updated in turn by using an alternate direction multiplier method framework, the original problem is decomposed into a low rank recovery problem and an adaptive beam forming problem, and then the low rank recovery problem is converted into an augmented Lagrange function form.
4. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 3, wherein,
the step 4 is specifically as follows: optimizing the augmented Lagrangian function in the step 3 by using an alternate direction multiplier method, optimizing an interference signal matrix by using a singular value threshold method, optimizing an introduced auxiliary variable matrix by using a Lagrangian multiplier method, optimizing a real echo signal matrix by using a soft threshold method, optimizing a weight vector of an array antenna by using a linear constraint least variance method (LCMV), optimizing the rest Lagrangian multiplier and penalty parameters, and repeating the variable optimization process until convergence conditions are met or the maximum iteration times are reached.
5. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar as claimed in claim 4, wherein,
the step 5 is specifically as follows: SAR imaging is carried out by using the real echo signals obtained in the step 4, which are subjected to interference suppression and self-adaptive beam forming, so that a final imaging result is obtained, and the imaging process is similar to the coherent accumulation focusing process carried out by a conventional SAR system.
6. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 5, wherein,
in step 2, the form is as follows:
wherein:
representing the original echo signal matrix of the input, +.>
Matrix of real echo signals representing no interference, +.>
Representing an interference signal matrix>
For the introduced auxiliary variable matrix, +.>
Representing an operation of stacking vectors as a matrix,/->
Weighting vectors for arrays>
Indicating beam pointing +.>
Array steering vector of angle, +.>
For the weight of the constraint term, +.>
、
、/>
Respectively representing the computing kernel norms,/->
Norms +.>
Manipulation of norms, ++>
As an arbitrarily small positive real number, superscript ++in the formula>
Representing the conjugate transpose operation.
7. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 6, wherein,
the augmented Lagrangian equation in step 3 is
Wherein->
Is Lagrangian multiplier, +.>
And 5, setting punishment super parameters.
8. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 7, wherein,
the optimization problem solving sequence of the variables in the step 4 is that firstly, an interference matrix is optimized, then an auxiliary variable matrix is optimized, then a signal matrix and an array channel weighting vector are optimized, finally, lagrange multiplier and punishment super-parameters introduced in an augmented Lagrange equation are optimized, after each calculation is completed through an alternate direction multiplier method frame, the result of the iteration is substituted into a calculation formula of the next iteration for updating the variables, wherein the singular value threshold method is used for solving a closed solution by loosening a non-convex optimization problem into a convex optimization problem and approximating the convex optimization problem with an F norm, the soft threshold method is used for differentiating the multi-variable optimization problem by constructing the monotonic function, the solution is convenient, the LCMV method is a traditional array signal self-adaptive filtering algorithm, and the integral power anti-interference is reduced by limiting the expected direction gain to be constant.
9. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 8, wherein,
solving the variables in step 4
The iterative closed-form solution of the optimization problem of (2) is:
10. The method for suppressing space-time interference of a novel multi-channel synthetic aperture radar according to claim 9, wherein,
solving the variables in step 4
An iterative closed-form solution to the optimization problem of +.>
A soft threshold function is used to assist the solution, wherein +.>
Representing the real signal matrix estimation result of the next iteration, a>
The loss function representing the current iteration calculation, the superscript of all parameters representing the iteration round, ST representing the soft threshold function +.>
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103901416A (en) * | 2014-03-31 | 2014-07-02 | 西安电子科技大学 | Multichannel clutter suppression method based on robust principal component analysis |
| CN108318865A (en) * | 2018-01-22 | 2018-07-24 | 西安电子科技大学 | Multichannel SAR cheating interferences differentiate and Adaptive Suppression method |
| CN110988854A (en) * | 2019-12-24 | 2020-04-10 | 西安电子科技大学 | Robust self-adaptive beam forming algorithm based on alternative direction multiplier method |
| CN111273238A (en) * | 2020-01-06 | 2020-06-12 | 中国航天科工集团八五一一研究所 | SAR (synthetic aperture radar) wide-band and narrow-band interference simultaneous inhibition method based on low-rank recovery |
| CN111812648A (en) * | 2020-07-22 | 2020-10-23 | 东南大学 | Multichannel synthetic aperture radar RPCA amplitude-phase combined target detection method and device |
| CN115184877A (en) * | 2022-06-21 | 2022-10-14 | 苏州大学 | Multi-parameter optimization SAR anti-interference method based on RD imaging |
| WO2022271601A1 (en) * | 2021-06-23 | 2022-12-29 | Mayo Foundation For Medical Education And Research | Systems and methods for reverberation clutter artifacts suppression in ultrasound imaging |
-
2023
- 2023-03-16 CN CN202310252878.3A patent/CN116068500B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103901416A (en) * | 2014-03-31 | 2014-07-02 | 西安电子科技大学 | Multichannel clutter suppression method based on robust principal component analysis |
| CN108318865A (en) * | 2018-01-22 | 2018-07-24 | 西安电子科技大学 | Multichannel SAR cheating interferences differentiate and Adaptive Suppression method |
| CN110988854A (en) * | 2019-12-24 | 2020-04-10 | 西安电子科技大学 | Robust self-adaptive beam forming algorithm based on alternative direction multiplier method |
| CN111273238A (en) * | 2020-01-06 | 2020-06-12 | 中国航天科工集团八五一一研究所 | SAR (synthetic aperture radar) wide-band and narrow-band interference simultaneous inhibition method based on low-rank recovery |
| CN111812648A (en) * | 2020-07-22 | 2020-10-23 | 东南大学 | Multichannel synthetic aperture radar RPCA amplitude-phase combined target detection method and device |
| WO2022271601A1 (en) * | 2021-06-23 | 2022-12-29 | Mayo Foundation For Medical Education And Research | Systems and methods for reverberation clutter artifacts suppression in ultrasound imaging |
| CN115184877A (en) * | 2022-06-21 | 2022-10-14 | 苏州大学 | Multi-parameter optimization SAR anti-interference method based on RD imaging |
Non-Patent Citations (2)
| Title |
|---|
| 傅东宁等: "基于图拉普拉斯嵌入的合成孔径雷达时变窄带干扰抑制算法", 《系统工程与电子技术》, pages 1846 - 1853 * |
| 黄岩: "复杂电磁环境下合成孔径雷达动目标检测与识别方法研究", 《中国博士学位论文全文数据库信息科技辑》, pages 29 - 52 * |
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