A sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition

Abstract

The present invention discloses a sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, comprising: performing STFT on the echo signal to obtain a time-frequency spectrum, calculating the instantaneous energy of each time-frequency point; equally dividing the frequency interval of the time-frequency spectrum, calculating the energy proportion of the frequency subinterval to the total frequency interval at each time point; for each time point, selecting the amplitude mean of the frequency subinterval with the largest energy proportion as the filtering threshold, and filtering the time-frequency point at that time point using the threshold; performing an inverse short-time Fourier transform on the filtered time-frequency spectrum, and performing singular value decomposition on the obtained echo signal matrix; selecting a singular value set representing the clutter from the singular value decomposition result, and constructing a signal subspace matrix based on the singular value set; projecting the echo signal matrix onto the signal subspace matrix to obtain the echo signal after clutter suppression. The present invention achieves effective sea clutter suppression in a strong sea clutter environment, improving the radar's detection capability for weak targets.

Description

Technical Field

[0001] The present invention belongs to the field of radar technology, and in particular relates to a sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition. Background Art

[0002] When radar processes received signals, clutter components can adversely affect target signal detection. If not suppressed, targets cannot be accurately and effectively detected. Therefore, how to effectively suppress clutter and improve the detection performance of slow-moving targets in strong clutter environments is of great significance.

[0003] In a real sea clutter environment, sea clutter interference is intense, the types of interference encountered by radars are complex, and the center frequency of the spectrum varies greatly, often exhibiting non-Gaussian and non-stationary characteristics. At the same time, low-speed targets on the sea surface have a small Doppler shift, resulting in significant overlap with strong and low-frequency clutter in the Doppler domain.

[0004] Conventional clutter suppression methods, such as MTI (Moving Target Indication) and MTD (Moving Target Detection) algorithms, can filter out clutter in the time or frequency domain. However, when detecting slow-moving targets, it is difficult to distinguish between the target and clutter signals when there is significant overlap between the target and zero-frequency clutter in the Doppler domain. Frequency-domain filter-based clutter suppression methods, such as two-pulse cancellation, also struggle to effectively suppress sea clutter when there is significant overlap between the target and zero-frequency clutter in the Doppler domain. Sira et al. proposed a subspace-based sea clutter suppression technique that estimates the sea clutter subspace. Subspace-based algorithms are essentially based on signal characteristics. By performing eigendecomposition on the echo signal matrix, they isolate the clutter subspace, thereby suppressing the clutter component in the signal and highlighting the target. The choice of clutter subspace directly influences the suppression effect. Rafaat Khan et al. proposed a sea clutter suppression technique based on singular value decomposition (SVD). However, this algorithm struggles to effectively suppress sea clutter when its Bragg peaks are not prominent. Furthermore, when using the SVD algorithm for clutter suppression, the choice of singular values ​​can affect the generation of the clutter subspace, thus affecting the suppression effect. This can lead to incomplete clutter removal or the suppression of weak targets as clutter. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] A sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, comprising:

[0008] A. performing a short-time Fourier transform on the echo signal containing clutter to obtain a time-frequency spectrum of the echo signal, and calculating the instantaneous energy of each time-frequency point of the time-frequency spectrum;

[0009] B. Equally divide the frequency interval of the time-frequency spectrum. For each time point of the time-frequency spectrum, calculate the energy proportion of each frequency subinterval at that time point to the total frequency interval.

[0010] C. For each time point of the time-frequency spectrum, select the amplitude mean of the frequency subinterval with the largest energy proportion at that time point as the filtering threshold, and use the filtering threshold to filter the time-frequency point at that time point to obtain the filtered time-frequency spectrum;

[0011] D. performing an inverse short-time Fourier transform on the filtered time-frequency spectrum, and performing a singular value decomposition on the transformed echo signal matrix to obtain a singular value decomposition result;

[0012] E. Selecting a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set;

[0013] F. Projecting the echo signal matrix into the signal subspace matrix to obtain an echo signal after clutter suppression.

[0014] Optionally, the sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition further includes:

[0015] After step C and before step D, determine whether there is still clutter interference in the filtered time-frequency spectrum; if there is still clutter interference, use the filtered time-frequency spectrum as the input of step B and repeat steps B to C; if there is no clutter interference, continue to step D.

[0016] Optionally, the selecting of singular values ​​of a plurality of clutter subspaces from the singular value decomposition result to form a singular value set representing the clutter includes:

[0017] A clutter basis selection method based on K-means clustering is used to select a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter.

[0018] Optionally, the selecting of singular values ​​of a plurality of clutter subspaces from the singular value decomposition result to form a singular value set representing the clutter includes:

[0019] By using a method of obtaining a difference spectrum, a number of singular values ​​of the clutter subspace are selected from the singular value decomposition result to form a singular value set representing the clutter.

[0020] Optionally, constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set includes:

[0021] Based on the singular value set, the vector product of the singular vectors representing the clutter subspace is calculated, and the calculation formula is: ;

[0022] According to the singular vector vector product, the signal subspace matrix orthogonal to the clutter subspace is constructed using the least squares method. The calculation formula is: ;

[0023] in, represents the set of singular values, The singular value decomposition result corresponds to The first The left singular vector of singular values, The singular value decomposition result corresponds to The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-vector product, is the identity matrix, represents the signal subspace matrix.

[0024] Optionally, the sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition is applied to radar.

[0025] The present invention also provides a sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition, comprising:

[0026] a first calculation module, configured to perform a short-time Fourier transform on the echo signal containing clutter to obtain a time-frequency spectrum of the echo signal and calculate the instantaneous energy of each time-frequency point of the time-frequency spectrum;

[0027] The second calculation module is used to equally divide the frequency interval of the time-frequency spectrum and calculate the energy ratio of each frequency subinterval at each time point in the time-frequency spectrum to the total frequency interval;

[0028] The filtering module is used to select the amplitude mean of the frequency subinterval with the largest energy proportion at each time point of the time-frequency spectrum as the filtering threshold, and use the filtering threshold to filter the time-frequency point at the time point to obtain the filtered time-frequency spectrum;

[0029] The third calculation module is used to perform an inverse short-time Fourier transform on the filtered time-frequency spectrum and perform a singular value decomposition on the echo signal matrix obtained by the transformation to obtain a singular value decomposition result;

[0030] A construction module, configured to select a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and construct a signal subspace matrix orthogonal to the clutter subspace based on the singular value set;

[0031] The matrix projection module is used to project the echo signal matrix into the signal subspace matrix to obtain the echo signal after clutter is suppressed.

[0032] Optionally, the device further includes: a judgment module;

[0033] The judgment module is configured to determine whether clutter interference still exists in the filtered time-frequency spectrum after the filtering module is triggered and before the third calculation module is triggered; if clutter interference still exists, use the filtered time-frequency spectrum as input to the second calculation module to re-trigger the second calculation module and the filtering module; if clutter interference does not exist, continue to trigger the third calculation module.

[0034] Optionally, the building block includes:

[0035] The first calculation submodule is used to calculate the vector product of the singular vectors representing the clutter subspace based on the singular value set. The calculation formula is: ;

[0036] The second calculation submodule is used to construct a signal subspace matrix orthogonal to the clutter subspace using the least squares method according to the singular vector vector product. The calculation formula is: ;

[0037] in, represents the set of singular values, The singular value decomposition result corresponds to The first The left singular vector of singular values, The singular value decomposition result corresponds to The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-vector product, is the identity matrix, represents the signal subspace matrix.

[0038] The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition provided by the present invention combines time-frequency spectrum energy distribution with singular value decomposition to improve the sea clutter suppression processing method. It can effectively filter out sea clutter signals and protect the signal energy of low-speed targets. While filtering out interference signals, it can also reduce the loss of effective signals and improve the target signal-to-noise ratio. This overcomes the problem of poor sea clutter suppression effect in strong sea clutter environments and the tendency of weak targets to be suppressed as clutter in the prior art, thereby improving radar detection capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition provided by an embodiment of the present invention;

[0040] Figure 2 This is a flow chart of another sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition provided by an embodiment of the present invention;

[0041] Figure 3 This is a distance-time diagram of the echo signal before sea clutter suppression;

[0042] Figure 4 To use the traditional SVD sea clutter suppression method Figure 3 The distance-time diagram of the echo signal after sea clutter suppression;

[0043] Figure 5 To use the sea clutter suppression method of the present invention to Figure 3 The distance-time diagram of the echo signal after sea clutter suppression;

[0044] Figure 6 This is a block diagram of a sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0046] In order to achieve effective sea clutter suppression in a strong sea clutter environment and improve the radar's ability to detect weak targets, an embodiment of the present invention provides a sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, such as Figure 1 As shown, the method includes the following steps:

[0047] A. Perform short-time Fourier transform on the echo signal containing clutter to obtain the time-frequency spectrum of the echo signal and calculate the instantaneous energy of each time-frequency point of the time-frequency spectrum.

[0048] Specifically, the formula for performing short-time Fourier transform on the echo signal containing clutter is:

[0049] ;

[0050] in, Represents the echo signal, is the window function, and its superscript represents the conjugation operation, m 、 n is an integer, is the natural base, is the imaginary unit, is the frequency sampling period, is the time sampling period, The time-frequency spectrum time-frequency points.

[0051] The calculation method of the instantaneous energy of each time-frequency point of the time-spectrum is as follows:

[0052] ,

[0053] ; ;

[0054] in, Indicates the time spectrum in time, The time-frequency point at the frequency point; Indicates the time spectrum at time point, The instantaneous energy at a frequency point, Indicates time, Indicates frequency.

[0055] B. Equally divide the frequency interval of the time-frequency spectrum. For each time point of the time-frequency spectrum, calculate the energy proportion of each frequency sub-interval at that time point to the total frequency interval.

[0056] Specifically, the frequency interval of the time spectrum is divided into N frequency subintervals, each frequency subinterval has m / N frequency points, and then calculate the energy proportion of each frequency subinterval in the total frequency interval at each time point. The calculation method is as follows:

[0057] ;

[0058] in, Indicates the At the time point The energy of the frequency subinterval, Indicates the The energy of the total frequency interval at a time point, is the calculated energy ratio.

[0059] C. For each time point of the time-frequency spectrum, the amplitude mean of the frequency sub-interval with the largest energy proportion at that time point is selected as the filtering threshold, and the time-frequency point at that time point is filtered using the filtering threshold to obtain the filtered time-frequency spectrum.

[0060] Specifically, the amplitude mean of the frequency sub-interval with the largest energy proportion at each time point is selected as the filtering threshold. The reason is that this interval is severely interfered by the strong interference signal, so the amplitude mean of this interval is calculated as the filtering threshold.

[0061] Then, for the time spectrum At a time point, the amplitude of each time-frequency point at that time point is compared with the filtering threshold at that time point. If the amplitude is greater than the filtering threshold, the signal of the time-frequency point is filtered out; otherwise, the signal of the time-frequency point is retained. The filtering process is implemented as follows:

[0062] ;

[0063] in, is the filtering factor. If the amplitude of the time-frequency point is greater than the filtering threshold, then Set to 0; if the amplitude of the time-frequency point is not greater than the filtering threshold, then Set to 1; Indicates the time-frequency spectrum after filtering. time point, The time-frequency points of the frequency points.

[0064] It can be understood that, through step C, adaptive time-frequency filtering can be implemented on the time-frequency spectrum, thereby filtering out interference signals while reducing the loss of effective signals.

[0065] D. Perform inverse short-time Fourier transform on the filtered time-frequency spectrum, and perform singular value decomposition on the transformed echo signal matrix to obtain a singular value decomposition result.

[0066] Among them, the formula for inverse short-time Fourier transform of the filtered time-frequency spectrum is:

[0067] ;

[0068] in, Represents the filtered echo signal.

[0069] The filtered echo signal forms an echo signal matrix , then Perform singular value decomposition; its implementation is as follows:

[0070] ;

[0071] in, represents the left unitary matrix in the singular value decomposition result, represents the right unitary matrix in the singular value decomposition result; Represents the diagonal matrix in the singular value decomposition result, which is All elements except the main diagonal are 0. The elements on the main diagonal are The singular values ​​of , and the singular values ​​on the main diagonal are arranged in descending order from large to small; represents the set of singular values ​​of the clutter subspace, represents the set of singular values ​​of the target subspace, represents the set of singular values ​​of the noise subspace; for Belong to No. singular values, for Corresponding Lidi The left singular vector of singular values, for Corresponding Lidi the right singular vector of singular values; for Belong to No. singular values, for Corresponding Lidi The left singular vector of singular values, for Corresponding Lidi the right singular vector of singular values; for Belong to No. singular values, for Corresponding Lidi The left singular vector of singular values, for Corresponding Lidi The right singular vector of singular values.

[0072] E. Selecting several singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set.

[0073] It can be understood that the singular values ​​of several clutter subspaces are selected from the singular value decomposition results to form a singular value set representing the clutter. C , specifically from the diagonal matrix It is selected from the main diagonal line, and there are many ways to select it.

[0074] Exemplarily, in one implementation, selecting singular values ​​of several clutter subspaces from the singular value decomposition result to form a singular value set representing the clutter may include:

[0075] Using the method of finding the difference spectrum, the singular values ​​of several clutter subspaces are selected from the singular value decomposition results to form a singular value set that characterizes the clutter. C .

[0076] Specifically, using the method of finding the difference spectrum, we can calculate The difference between the adjacent singular values ​​on the main diagonal of , thus selecting several singular values ​​with larger order from the main diagonal to form the singular value set of the clutter subspace C It can be understood that since the singular values ​​on the main diagonal are arranged in descending order from large to small, when finding the difference between adjacent singular values, the smaller singular value in a pair of singular values ​​with a larger difference is considered to have a sudden drop in value, so several larger singular values ​​before the singular value are selected as the singular values ​​of the clutter subspace.

[0077] In another implementation, singular values ​​of several clutter subspaces are selected from the singular value decomposition result to form a singular value set representing the clutter, which may include:

[0078] Using the clutter basis selection method based on K-means clustering, the singular values ​​of several clutter subspaces are selected from the singular value decomposition results to form a singular value set that characterizes the clutter. C .

[0079] Specifically, the diagonal matrix Normalize the elements on the main diagonal of to obtain the singular value spectrum distribution characteristics; select the maximum singular value V1 from the singular value decomposition result, calculate the correlation coefficient between V1 and each right singular vector in the singular value decomposition result, as the spatial correlation characteristics of the singular vector; based on the above singular value spectrum distribution characteristics and the spatial correlation characteristics of the singular vector, use the K-means clustering algorithm to cluster them; then, select the cluster with larger singular value amplitude and stronger spatial correlation as the cluster representing the clutter component, so as to determine the singular value set representing the clutter component C .

[0080] Select the set of singular values C Then, based on the singular value set C Construct a signal subspace matrix orthogonal to the clutter subspace as follows:

[0081] (1) Based on singular value sets C , calculate the vector product of the singular vectors representing the clutter subspace, and the calculation formula is ;

[0082] (2) Based on the singular vector vector product, the least squares method is used to construct the signal subspace matrix orthogonal to the clutter subspace. The calculation formula is: ;

[0083] in, represents the set of singular values, The corresponding singular value decomposition result The first The left singular vector of singular values, The corresponding singular value decomposition result The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-cross product, is the identity matrix, Represents the constructed signal subspace matrix.

[0084] F. Project the echo signal matrix into the signal subspace matrix to obtain the echo signal after clutter suppression.

[0085] Specifically, the projection formula for projecting the echo signal matrix onto the signal subspace matrix is ​​as follows:

[0086] ;

[0087] in, represents the projection result, which is a matrix representing the echo signal after clutter suppression.

[0088] The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, provided by the embodiments of the present invention, combines time-frequency spectrum energy distribution with singular value decomposition to improve sea clutter suppression processing methods. This method effectively filters out sea clutter signals and protects the signal energy of low-speed targets. While filtering out interfering signals, it also reduces the loss of effective signals and improves the target signal-to-noise ratio. This method overcomes the existing problems of poor sea clutter suppression in strong sea clutter environments and the tendency to suppress weak targets as clutter, thereby enhancing radar detection capabilities.

[0089] In one embodiment, Figure 2 As shown, after step C and before step D, the sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition provided by the embodiment of the present invention may further include:

[0090] C': Determine whether there is still clutter interference in the filtered time-frequency spectrum; if there is still clutter interference, use the filtered time-frequency spectrum as the input of step B and repeat steps B to C; if there is no clutter interference, continue to step D.

[0091] The determination of whether clutter interference still exists in the filtered time-frequency spectrum can be achieved by monitoring the signal-to-clutter ratio of the signal.

[0092] Figure 3 shows a range-time diagram of the echo signal before sea clutter suppression;

[0093] Figure 4 To use the traditional SVD sea clutter suppression method Figure 3 The distance-time diagram of the echo signal after sea clutter suppression;

[0094] Figure 5 To use the sea clutter suppression method of the present invention to Figure 3 The distance-time diagram of the echo signal after sea clutter suppression;

[0095] contrast Figures 3-5 It can be seen that the sea clutter suppression method of the present invention is significantly better than the traditional SVD method in sea clutter suppression effect. This proves that the embodiment of the present invention can achieve effective sea clutter suppression in a strong sea clutter environment, improving the radar's detection capability for weak targets.

[0096] The embodiments of the present invention can be applied to radar systems of various platforms such as airborne and shipborne platforms to participate in signal clutter suppression processing, and can also be applied to early warning aircraft for sea detection and reconnaissance, as well as to radar seekers for detecting and striking sea surface targets.

[0097] Corresponding to the above-mentioned sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, the embodiment of the present invention further provides a sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition, see Figure 6 As shown, the device includes:

[0098] The first calculation module 601 is configured to perform a short-time Fourier transform on the echo signal containing clutter to obtain a time-frequency spectrum of the echo signal and calculate the instantaneous energy of each time-frequency point of the time-frequency spectrum;

[0099] The second calculation module 602 is configured to equally divide the frequency interval of the time-frequency spectrum and calculate, for each time point of the time-frequency spectrum, the energy ratio of each frequency subinterval at that time point to the total frequency interval;

[0100] A filtering module 603 is configured to select, for each time point of the time-frequency spectrum, the amplitude mean of the frequency subinterval with the largest energy proportion at that time point as a filtering threshold, and filter the time-frequency point at that time point using the filtering threshold to obtain a filtered time-frequency spectrum;

[0101] The third calculation module 604 is used to perform an inverse short-time Fourier transform on the filtered time-frequency spectrum and perform a singular value decomposition on the echo signal matrix obtained by the transformation to obtain a singular value decomposition result;

[0102] A construction module 605 is configured to select a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and construct a signal subspace matrix orthogonal to the clutter subspace based on the singular value set;

[0103] The matrix projection module 606 is configured to project the echo signal matrix into the signal subspace matrix to obtain an echo signal after clutter is suppressed.

[0104] Optionally, the above device may further include: a judgment module;

[0105] The judgment module is configured to determine whether clutter interference still exists in the filtered time-frequency spectrum after the filtering module 603 is triggered and before the third calculation module 604 is triggered; if clutter interference still exists, use the filtered time-frequency spectrum as the input of the second calculation module 602 to re-trigger the second calculation module 602 and the filtering module 603; if clutter interference does not exist, continue to trigger the third calculation module 604.

[0106] Optionally, building block 605 includes:

[0107] The first calculation submodule is used to calculate the vector product of the singular vectors representing the clutter subspace based on the singular value set. The calculation formula is: ;

[0108] The second calculation submodule is used to construct a signal subspace matrix orthogonal to the clutter subspace using the least squares method according to the singular vector vector product. The calculation formula is: ;

[0109] in, represents the set of singular values, The singular value decomposition result corresponds to The first The left singular vector of singular values, The singular value decomposition result corresponds to The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-vector product, is the identity matrix, represents the signal subspace matrix.

[0110] The present invention also provides a computer-readable storage medium having a computer program stored therein, which, when executed by a processor, implements the method steps described in any of the above-mentioned sea clutter suppression methods based on time-frequency spectrum energy distribution and singular value decomposition.

[0111] Optionally, the computer-readable storage medium may be a non-volatile memory (NVM), such as at least one disk memory.

[0112] In another embodiment of the present invention, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the method steps of any of the above-mentioned sea clutter suppression methods based on time-spectral energy distribution and singular value decomposition.

[0113] It should be noted that, for the storage medium / computer program product embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0114] It should be noted that the terms "first," "second," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0115] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0116] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings and the disclosure. In the description of the present invention, the word "comprising" does not exclude other components or steps, "one" or "a" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0117] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, devices (equipment), or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments, all of which are collectively referred to herein as "modules" or "systems." Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The computer program may be stored / distributed in a suitable medium, provided together with other hardware or as part of the hardware, or may be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0118] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (devices) and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition, characterized in that: include: A. performing a short-time Fourier transform on the echo signal containing clutter to obtain a time-frequency spectrum of the echo signal, and calculating the instantaneous energy of each time-frequency point of the time-frequency spectrum; B. Equally divide the frequency interval of the time-frequency spectrum. For each time point of the time-frequency spectrum, calculate the energy proportion of each frequency subinterval at that time point to the total frequency interval. C. For each time point of the time-frequency spectrum, select the amplitude mean of the frequency subinterval with the largest energy proportion at that time point as the filtering threshold, and use the filtering threshold to filter the time-frequency point at that time point to obtain the filtered time-frequency spectrum; D. performing an inverse short-time Fourier transform on the filtered time-frequency spectrum, and performing a singular value decomposition on the transformed echo signal matrix to obtain a singular value decomposition result; E. Selecting a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set; F. Projecting the echo signal matrix into the signal subspace matrix to obtain an echo signal after clutter suppression.

2. The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition according to claim 1 is characterized in that: Also includes: After step C and before step D, determine whether clutter interference still exists in the filtered time-frequency spectrum; If there is still clutter interference, use the filtered time-frequency spectrum as the input of step B and repeat steps B to C. If there is no clutter interference, continue to step D.

3. The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition according to claim 1 is characterized in that: The step of selecting a plurality of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter includes: A clutter basis selection method based on K-means clustering is used to select a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter.

4. The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition according to claim 1 is characterized in that: The step of selecting a plurality of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter includes: By using a method of obtaining a difference spectrum, a number of singular values ​​of the clutter subspace are selected from the singular value decomposition result to form a singular value set representing the clutter.

5. The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition according to claim 1, characterized in that: Constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set, including: Based on the singular value set, the vector product of the singular vectors representing the clutter subspace is calculated, and the calculation formula is: ; According to the singular vector vector product, the signal subspace matrix orthogonal to the clutter subspace is constructed using the least squares method. The calculation formula is: ; in, represents the set of singular values, The singular value decomposition result corresponds to The first The left singular vector of singular values, The singular value decomposition result corresponds to The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-vector product, is the identity matrix, represents the signal subspace matrix.

6. The sea clutter suppression method based on time-frequency spectrum energy distribution and singular value decomposition according to claim 1, characterized in that: Used in radar.

7. A sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition, characterized in that: include: a first calculation module, configured to perform a short-time Fourier transform on the echo signal containing clutter to obtain a time-frequency spectrum of the echo signal and calculate the instantaneous energy of each time-frequency point of the time-frequency spectrum; The second calculation module is used to equally divide the frequency interval of the time-frequency spectrum and calculate the energy ratio of each frequency subinterval at each time point in the time-frequency spectrum to the total frequency interval; The filtering module is used to select the amplitude mean of the frequency subinterval with the largest energy proportion at each time point of the time-frequency spectrum as the filtering threshold, and use the filtering threshold to filter the time-frequency point at the time point to obtain the filtered time-frequency spectrum; The third calculation module is used to perform an inverse short-time Fourier transform on the filtered time-frequency spectrum and perform a singular value decomposition on the echo signal matrix obtained by the transformation to obtain a singular value decomposition result; A construction module, configured to select a number of singular values ​​of the clutter subspace from the singular value decomposition result to form a singular value set representing the clutter, and construct a signal subspace matrix orthogonal to the clutter subspace based on the singular value set; The matrix projection module is used to project the echo signal matrix into the signal subspace matrix to obtain the echo signal after clutter is suppressed.

8. The sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition according to claim 7, characterized in that: Also includes: Judgment module; The judgment module is configured to determine whether clutter interference still exists in the filtered time-frequency spectrum after the filtering module is triggered and before the third calculation module is triggered; if clutter interference still exists, use the filtered time-frequency spectrum as input to the second calculation module to re-trigger the second calculation module and the filtering module; if clutter interference does not exist, continue to trigger the third calculation module.

9. The sea clutter suppression device based on time-frequency spectrum energy distribution and singular value decomposition according to claim 7, characterized in that: The building blocks include: The first calculation submodule is used to calculate the vector product of the singular vectors representing the clutter subspace based on the singular value set. The calculation formula is: ; The second calculation submodule is used to construct a signal subspace matrix orthogonal to the clutter subspace using the least squares method according to the singular vector vector product. The calculation formula is: ; in, represents the set of singular values, The singular value decomposition result corresponds to The first The left singular vector of singular values, The singular value decomposition result corresponds to The first The right singular vector of singular values, superscript represents the conjugate transpose of the matrix, represents the singular vector-vector product, is the identity matrix, represents the signal subspace matrix.

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