Clutter suppression method based on low-rank approximate point iteration

Abstract

The invention discloses a clutter suppression method based on low-rank approximate point iteration, which comprises the steps of processing radar echo data to obtain baseband data containing clutter signals, processing the baseband data containing the clutter signals according to a set coherent processing interval, constructing a distance Doppler two-dimensional matrix and obtaining a sparse expression of the distance Doppler two-dimensional matrix; and finally, carrying out iterative solution according to a low-rank approximate point iterative algorithm, substituting the solved data into a distance Doppler two-dimensional matrix, and carrying out parameter detection and estimation on the distance and the speed of the target. According to the scheme, the sparse characteristic of the target and the low-rank performance dual constraint of clutter are utilized, the moving target detection probability is improved by adopting a low-rank approximate point iteration method, the problem of moving target detection under the influence of clutter in a complex natural environment can be solved while clutter signals are effectively suppressed, the clutter signal suppression effect is effectively improved, and the moving target detection efficiency is improved. And the target discovery probability is greatly improved.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing, specifically relating to a clutter suppression method based on low-rank approximation point iteration. Background Technology

[0002] Detecting the range and velocity parameters of weak targets in cluttered environments has become a research hotspot and challenge in the field of target detection using signal processing. The environment in which weak targets exist is complex and variable. The interaction of natural geographical and hydrological environments such as ground clutter, ocean clutter, and meteorological clutter, along with the expansion of the clutter velocity spectrum, renders traditional moving target detection methods ineffective. Traditional detection algorithms reduce the target detection probability, leading to a further increase in the false alarm rate. While sparse recovery methods are popular for detecting weak moving targets, current methods only consider the sparsity of the target and have stringent requirements regarding the distribution of the clutter environment and specific prior information. This results in a lack of transferability in target detection performance, severely limiting its application scenarios, especially in engineering applications where simplicity and ease of operation are crucial. Therefore, effectively suppressing clutter signals while extracting the target's range and velocity parameters, and ensuring ease of engineering implementation, are urgent problems to be solved in the detection of weak targets in complex cluttered environments. By leveraging the sparse characteristics of targets and the low-rank behavior of clutter as dual constraints, and employing a low-rank approximation point iteration method, the probability of moving target detection is improved.

[0003] In existing technologies, Chinese patent document CN114675252A proposes a leaf cluster clutter suppression method and system based on low-rank sparse matrix constraint optimization. This method mainly utilizes fractional Fourier transform to estimate the order, then constructs a low-rank sparse constraint model in the fractional Fourier transform domain to construct the cost function, and finally solves the target estimation parameters using simulated annealing optimization improved augmented Lagrange algorithm. However, this method uses fractional Fourier transform, and the choice of the order has a certain degree of randomness, which will affect the convergence of subsequent calculations. At the same time, fractional Fourier transform is difficult to implement in engineering. In addition, the stability of simulated annealing optimization improved augmented Lagrange algorithm is affected by data, and occasional failures may occur. This phenomenon will seriously affect the target detection probability, so this method does not have universality.

[0004] A method for suppressing clutter from ground-penetrating radar (GPR) on airport runways was proposed in Computer Applications 2025, 0508, pp:1-7. This method mainly employs a deep learning network approach for target detection and estimation after clutter suppression, effectively addressing the complexity of manual parameter adjustment. However, this algorithm firstly limits the operating modes of GPR and cannot be transferred to other radar applications. Secondly, it cannot overcome the drawback of learning from massive amounts of data using deep learning networks, and it also has limitations in real-time performance. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a clutter suppression method based on low-rank approximation point iteration, which solves the problem of clutter overwhelming the detection of weak moving targets in complex natural environments, effectively suppresses clutter signals, and improves the target detection probability.

[0006] The specific technical solution for achieving the objective of this invention is as follows:

[0007] A clutter suppression method based on low-rank approximation point iteration includes the following steps:

[0008] Step 1: Process the echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and then perform digital down-conversion to obtain baseband data containing clutter signals.

[0009] Step 2: Construct a two-dimensional range-Doppler matrix based on the pulse number, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, according to the set coherent processing interval.

[0010] Step 3: Combining the range-Doppler two-dimensional matrix, further form a sparse expression for the range-Doppler two-dimensional matrix based on the sparsity characteristics of the target;

[0011] Step 4: Construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and by solving for the target.

[0012] Step 5: Iteratively solve the cost function using the low-rank approximation point iterative algorithm, and substitute the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] This invention relates to the problem of weak target detection in complex clutter environments. The solution of this invention utilizes the sparse characteristics of the target and the low-rank performance of the clutter as dual constraints. At the same time, it adopts the low-rank approximation point iteration method to improve the probability of moving target detection. It can effectively suppress clutter signals while extracting the distance and velocity parameters of the target signal. It is easy to implement in engineering and solves the problem of moving target detection under the influence of clutter in complex natural environments. It effectively improves the suppression of clutter signals and greatly improves the target detection probability.

[0015] The present invention will be further described below with reference to specific embodiments. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the clutter suppression method based on low-rank approximation point iteration of the present invention.

[0017] Figure 2 This is a two-dimensional Doppler image of the target range under conditions of complex natural environment and strong clutter, as described in an embodiment of the present invention.

[0018] Figure 3 This is a comparison diagram of the target detection probability after processing with the clutter suppression method based on low-rank approximation point iteration in the embodiments of the present invention and the traditional moving target detection method. Detailed Implementation

[0019] Example

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0022] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0023] Combination Figure 1 A clutter suppression method based on low-rank approximation point iteration includes the following steps:

[0024] Step 1: Process the echo data containing clutter to obtain intermediate frequency data containing clutter, and then perform digital down-conversion to obtain baseband data containing clutter.

[0025]

[0026] in, This represents the baseband data containing clutter signals obtained after digital down-conversion. Indicates clutter signal, Indicates the target signal. This represents a Gaussian noise signal.

[0027] Step 2: Construct a two-dimensional range-Doppler matrix based on the pulse number, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, according to the set coherent processing interval.

[0028] Let N be the number of pulses in a coherent processing interval, PRI be the pulse repetition period, and fs be the sampling rate. Therefore, the sampling interval is Ts = 1 / fs. Let M be the range gates of the distance-Doppler two-dimensional matrix. Based on the number of pulses in a coherent processing interval, the velocity gates are N. Then, the discretized expression of the baseband data containing clutter signals obtained after digital down-conversion of the echo data within the i-th (i = 1, 2, ..., N) repetition period PRI is:

[0029]

[0030] This yields a two-dimensional range-Doppler matrix for N pulses with a coherent processing interval:

[0031]

[0032] Where Ts represents the pulse sampling interval of the coherent processing interval, Ts=1 / fs, fs represents the sampling rate of the coherent processing interval, N represents the number of pulses in the coherent processing interval, and M is the set range gate of the range-Doppler two-dimensional matrix. This represents the echo data within the i-th repetition cycle PRI.

[0033] Step 3: Combining the range-Doppler two-dimensional matrix, and based on the sparsity characteristics of the target, further formulate the sparse expression of the range-Doppler two-dimensional matrix:

[0034]

[0035] Where X represents the sparse expression of the distance-Doppler two-dimensional matrix, which is an M-row N-column two-dimensional matrix, and D is an M-row N-column echo signal sparse recovery dictionary matrix, where... This represents an M-row, N-column sparse dictionary matrix containing clutter signals. For an M-row N-column sparse recovery dictionary matrix containing the target signal, Let be an M-row, N-column matrix of the scattered intensity of the echo signal, where This represents an M-row, N-column scattering intensity matrix containing clutter signals. This represents an M-row, N-column scattering intensity matrix containing the target signal. This represents an M-row, N-column Gaussian noise signal matrix.

[0036] Step 4: Construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and by solving for the target.

[0037] Assuming all targets in the entire radar observation space are point targets, exhibiting sparse characteristics in the range-Doppler two-dimensional matrix; clutter exhibits broadening characteristics in the Doppler velocity spectrum and low-rank characteristics in the range-Doppler two-dimensional matrix. The constructed cost function is as follows:

[0038]

[0039] in, Denotes the square of the Frobenius norm, with parameters This represents the regularization parameter for the corresponding clutter component. This represents the low-rank constraint of the corresponding clutter signal, and the parameters are... This represents the regularization parameter corresponding to the target signal. This represents the data sparsity constraint corresponding to the target signal.

[0040] Step 5: Iteratively solve the cost function using the low-rank approximation point iterative algorithm, and substitute the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

[0041] Introducing clutter approximation points and target approximation point Transform the cost function into:

[0042]

[0043] in, This represents the weighting coefficients corresponding to the clutter signal. Represents the weighting coefficients corresponding to the target signal;

[0044] Set the step size parameter as follows The identity matrix is The specific values ​​of the scattering intensity matrices of the target signal component and clutter component are solved according to the following iterative formula. Then, based on the specific values ​​of the scattering intensity matrices, conventional signal processing moving target detection algorithms are used to estimate the target range and velocity parameters:

[0045] The number of iterations is set to R, and the initial iteration values ​​of the scattering intensity matrices of clutter and target signal are: and The iterative formula is:

[0046] ,

[0047]

[0048] The iteration number ranges from k=0,1,…,R, and the operator... Represents the conjugate transpose of a matrix;

[0049] The iteration is performed according to the above iterative formula. When the values ​​of the scattering intensity matrices of the target signal component and clutter component obtained by the iterative calculation are stable (i.e., the difference before and after the calculation is less than the set threshold) or the number of iterations is reached, the iteration ends and the current scattering intensity matrices of the target signal component and clutter component are obtained.

[0050] After solving using the above iterative formula, the range-Doppler two-dimensional matrix can be substituted into conventional algorithms to detect and estimate the target range and velocity parameters, thereby achieving clutter suppression.

[0051] In this embodiment, it is assumed that a simulated device for detecting moving targets has 16 pulses in one coherent processing interval, with a pulse repetition period set to 50µs. The device operates at a frequency of 30GHz and transmits a linear frequency modulated signal with a pulse width of 2µs and a sampling rate of 5MHz. The range gate is 30m, and the velocity gate is 6.25m / s. There are 5 targets in the observation scene, with range gates set to 23, 81, 148, 201, and 236, and velocity gates set to 8, 8, 7, 10, and 11, respectively. The target scattering intensities are 200, 200, 200, 600, and 800, and the simulated clutter is uniformly distributed in Doppler channels 2 to 16.

[0052] By comparing the results of the traditional moving target detection algorithm (MTD) with the method of the present invention, the effectiveness of the method of the present invention is finally analyzed;

[0053] like Figure 2 The figure shows a range Doppler comparison between the method of the present invention and the traditional moving target detection method in the above detection scenario. It can be seen from the figure that the method of the present invention can more clearly estimate the target's range and velocity parameters, while the clutter is suppressed to the greatest extent, which is beneficial to improving the target detection probability.

[0054] like Figure 3 The figure shows a comparison of the target detection probability statistics domain with the traditional MTD method after a simulated target has undergone 1000 Monte Carlo experiments, processed by a clutter suppression method based on low-rank approximation points, with the signal-to-clutter ratio varying from -30dB to -5dB. The figure demonstrates that, under the same signal-to-clutter ratio conditions, the method used in this invention can effectively improve the detection probability. This fully verifies the effectiveness and feasibility of the method presented in this invention.

[0055] This solution also provides a clutter suppression system based on low-rank approximation point iteration, including the following modules:

[0056] Data processing module: used to process echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and to perform digital down-conversion to obtain baseband data containing clutter signals;

[0057] A two-dimensional range-Doppler matrix is ​​constructed based on the number of pulses, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, with the set coherent processing interval.

[0058] By combining the two-dimensional range-Doppler matrix, a sparse expression for the two-dimensional range-Doppler matrix is ​​further formed based on the sparsity characteristics of the target.

[0059] Cost function construction module: used to construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and the solution of the target;

[0060] Solution module: Iteratively solves the cost function using the low-rank approximation point iterative algorithm, and substitutes the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

[0061] This solution also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0062] Step 1: Process the echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and then perform digital down-conversion to obtain baseband data containing clutter signals.

[0063] Step 2: Construct a two-dimensional range-Doppler matrix based on the pulse number, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, according to the set coherent processing interval.

[0064] Step 3: Combining the range-Doppler two-dimensional matrix, further form a sparse expression for the range-Doppler two-dimensional matrix based on the sparsity characteristics of the target;

[0065] Step 4: Construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and by solving for the target.

[0066] Step 5: Iteratively solve the cost function using the low-rank approximation point iterative algorithm, and substitute the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

[0067] This solution also provides a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the following steps:

[0068] Step 1: Process the echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and then perform digital down-conversion to obtain baseband data containing clutter signals.

[0069] Step 2: Construct a two-dimensional range-Doppler matrix based on the pulse number, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, according to the set coherent processing interval.

[0070] Step 3: Combining the range-Doppler two-dimensional matrix, further form a sparse expression for the range-Doppler two-dimensional matrix based on the sparsity characteristics of the target;

[0071] Step 4: Construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and by solving for the target.

[0072] Step 5: Iteratively solve the cost function using the low-rank approximation point iterative algorithm, and substitute the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

[0073] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A clutter suppression method based on low-rank approximation point iteration, characterized in that, Includes the following steps: Step 1: Process the echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and then perform digital down-conversion to obtain baseband data containing clutter signals. Step 2: Construct a two-dimensional range-Doppler matrix based on the pulse number, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, according to the set coherent processing interval. Step 3: Combining the range-Doppler two-dimensional matrix, further form a sparse expression for the range-Doppler two-dimensional matrix based on the sparsity characteristics of the target; Step 4: Construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and by solving for the target. Step 5: Iteratively solve the cost function using the low-rank approximation point iterative algorithm, and substitute the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

2. The clutter suppression method based on low-rank approximation point iteration according to claim 1, characterized in that, The baseband data containing clutter signals obtained in step 1 is as follows: ； in, This represents the baseband data containing clutter signals obtained after digital down-conversion. Indicates clutter signal, Indicates the target signal. This represents a Gaussian noise signal.

3. The clutter suppression method based on low-rank approximation point iteration according to claim 2, characterized in that, The construction of the two-dimensional distance-Doppler matrix in step 2 is specifically as follows: ； ； Where Ts represents the pulse sampling interval of the coherent processing interval, Ts=1 / fs, fs represents the sampling rate of the coherent processing interval, N represents the number of pulses in the coherent processing interval, and M is the set range gate of the range-Doppler two-dimensional matrix. This represents the echo data within the i-th repetition cycle PRI.

4. The clutter suppression method based on low-rank approximation point iteration according to claim 3, characterized in that, The sparse expression of the distance-Doppler two-dimensional matrix in step 3 is as follows: ； Where X represents the sparse expression of the distance-Doppler two-dimensional matrix, which is an M-row N-column two-dimensional matrix, and D is an M-row N-column echo signal sparse recovery dictionary matrix, where... This represents an M-row, N-column sparse dictionary matrix containing clutter signals. For an M-row N-column sparse recovery dictionary matrix containing the target signal, Let be an M-row, N-column matrix of the scattered intensity of the echo signal, where This represents an M-row, N-column scattering intensity matrix containing clutter signals. This represents an M-row, N-column scattering intensity matrix containing the target signal. This represents an M-row, N-column Gaussian noise signal matrix.

5. The clutter suppression method based on low-rank approximation point iteration according to claim 4, characterized in that, The cost function constructed in step 4 is: ； in, Denotes the square of the Frobenius norm, with parameters This represents the regularization parameter for the corresponding clutter component. This represents the low-rank constraint of the corresponding clutter signal, and the parameters are... This represents the regularization parameter corresponding to the target signal. This represents the data sparsity constraint corresponding to the target signal.

6. The clutter suppression method based on low-rank approximation point iteration according to claim 5, characterized in that, The iterative solution of the cost function in step 5 using the low-rank approximation point iterative algorithm is as follows: Introducing clutter approximation points and target approximation point Transform the cost function into: ； in, This represents the weighting coefficients corresponding to the clutter signal. Represents the weighting coefficients corresponding to the target signal; Set the step size parameter as follows The identity matrix is The specific values ​​of the scattering intensity matrices of the target signal component and clutter component are solved according to the following iterative formula; The number of iterations is set to R, and the initial iteration values ​​of the scattering intensity matrices of clutter and target signal are: and The iterative formula is: ； ； The iteration number ranges from k=0,1,…,R, and the operator... Represents the conjugate transpose of a matrix; The iteration is performed according to the above iterative formula. When the values ​​of the scattering intensity matrices of the target signal component and clutter component obtained by the iterative calculation are stable or the number of iterations is reached, the iteration ends and the current scattering intensity matrices of the target signal component and clutter component are obtained. Then, based on the specific values ​​of the scattering intensity matrices, the target distance and velocity parameters are estimated using a conventional signal processing moving target detection algorithm.

7. A clutter suppression system based on low-rank approximation point iteration, characterized in that, Includes the following modules: Data processing module: used to process echo data containing clutter signals to obtain intermediate frequency data containing clutter signals, and to perform digital down-conversion to obtain baseband data containing clutter signals; A two-dimensional range-Doppler matrix is ​​constructed based on the number of pulses, pulse repetition period, sampling rate, range gate, velocity gate related parameters, and baseband data containing clutter signals obtained after digital down-conversion, with the set coherent processing interval. By combining the two-dimensional range-Doppler matrix, a sparse expression for the two-dimensional range-Doppler matrix is ​​further formed based on the sparsity characteristics of the target. Cost function construction module: used to construct a cost function based on the sparse expression of the range Doppler two-dimensional matrix, combined with the signal characteristics of the target and clutter, and the solution of the target; Solution module: Iteratively solves the cost function using the low-rank approximation point iterative algorithm, and substitutes the solved data into the range-Doppler two-dimensional matrix to detect and estimate the target distance and velocity parameters.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-storable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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