A high-frequency sky-wave radar sea clutter suppression method based on space-time domain cascade processing

Abstract

The application discloses a high-frequency sky-wave radar sea clutter suppression method based on space-time domain cascade processing, and realizes space-time domain cascade method by performing steps of constructing a Doppler local processing domain, estimating a clutter covariance matrix, adaptively suppressing a Doppler velocity clutter, singular value decomposition and orthogonal projection on received echo data of the high-frequency sky-wave radar, so that better clutter suppression performance can be obtained. According to different characteristics of target and sea clutter in space-time distribution, the scheme of the application cascades Doppler local processing domain filtering and space domain singular value decomposition, simultaneously uses information of signals in two dimensions of space and Doppler, avoids target missing problems caused by coincidence of target direction and the strongest direction of sea clutter energy, alleviates target gain loss problems when the target and the sea clutter are in the same beam main lobe, improves signal utilization, and can effectively suppress the sea clutter.

Description

Technical Field

[0001] The present invention belongs to the field of over-the-horizon radar sea clutter suppression and target detection, and in particular relates to a high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing. Background Art

[0002] High-frequency ground-wave radar (HFSWR) is a hybrid over-the-horizon radar system that combines the advantages of both sky-wave and ground-wave radars. It enables long-range detection while maintaining a relatively stable propagation channel. However, due to ionospheric modulation, the first-order peak of sea clutter in the echo spectrum is significantly broadened, easily drowning out target points near the first-order peak. This severely impacts the radar's ability to detect slow-moving targets such as ships. To improve HFSWR's target detection performance, it is essential to effectively suppress the first-order sea clutter.

[0003] Existing sea clutter suppression algorithms for high-frequency radars can be categorized into four main types: those based on the statistical characteristics of sea clutter, time-frequency domain transform algorithms, subspace decomposition algorithms, and those based on space-time adaptive processing. Clutter suppression algorithms based on the statistical characteristics of sea clutter have high computational complexity and strong model dependence, resulting in poor clutter suppression performance under varying sea conditions. Space-time adaptive processing algorithms, due to their high computational complexity and computational complexity, place high demands on hardware. Time-frequency domain transform algorithms are complex to implement and may introduce artifacts or falsely suppress target signals. Subspace decomposition algorithms have high data requirements, requiring sufficient observation data to accurately estimate the signal and clutter subspaces, resulting in high computational complexity in high-dimensional space. Furthermore, for targets submerged in sea clutter, it is often difficult to distinguish them from the sea clutter. Therefore, while the sea clutter is suppressed, the target echo is often significantly attenuated, resulting in the failure of subsequent constant false alarm detection. How to effectively suppress clutter while preserving the target signal remains a major challenge for subspace-based methods. Summary of the Invention

[0004] In response to the above problems, the present invention aims to provide a method for suppressing sea clutter in a high-frequency sky-wave radar based on space-time domain cascade processing. By performing steps such as constructing a Doppler local processing domain, estimating the clutter covariance matrix, adaptive Doppler dimension clutter suppression, singular value decomposition, and orthogonal projection on the echo data received by the high-frequency sky-wave radar, the space-time domain cascade method is implemented to obtain better clutter suppression performance.

[0005] The specific technical solutions for achieving the purpose of the present invention are as follows:

[0006] A method for suppressing sea clutter in high-frequency ground-to-air wave radar based on space-time domain cascade processing comprises the following steps:

[0007] Step 1: Perform digital beamforming on the range element echo to be processed to obtain the space-time spectrum and determine the frequency distribution range of positive and negative first-order sea clutter;

[0008] Step 2: Select the neighboring range metadata of the current range element as the clutter sample, multiply the space-time slow-time snapshot data received by the radar with the conversion matrix to construct the Doppler local processing domain at a specific frequency.

[0009] Step 3: Estimate the clutter covariance matrix of the Doppler local domain of multiple adjacent range elements, sum them up and average them to obtain the estimated clutter covariance matrix of the range element to be processed R n ;

[0010] Step 4: Calculate the optimal weight vector and obtain the signal output by the array element at a specific Doppler frequency after filtering; traverse all Doppler frequencies of interest to obtain the output signal of sea clutter suppression in the entire Doppler domain;

[0011] Step 5: Perform singular value decomposition on the clutter samples processed in the time domain to construct a sea clutter subspace and a noise subspace. Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to suppress the sea clutter and obtain the output Y after sea clutter suppression.

[0012] Step 6: Use the scan vector to scan all angle ranges to obtain azimuth information, and cascade the spatial singular value decomposition and Doppler domain processing to obtain the array output after cascade processing, which is the output after suppressing sea clutter.

[0013] Step 7: Traverse all frequency points in the clutter area and scan the angle range of interest. Finally, restore the space-time data after clutter suppression to space-slow time data.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] (1) The solution of the present invention can effectively suppress sea clutter by cascading Doppler local processing domain filtering and spatial domain singular value decomposition based on the different characteristics of the target and sea clutter distribution in the space-time dimension;

[0016] (2) The high-frequency ground-to-sea wave radar sea clutter suppression method based on space-time domain cascade processing proposed in the present invention uses the information of the signal in both spatial and Doppler dimensions, thereby improving the utilization rate of the signal;

[0017] (3) The high-frequency ground-to-sea wave radar sea clutter suppression method based on space-time domain cascade processing proposed in the present invention avoids the problem of target cancellation that may be caused when the target azimuth coincides with the azimuth with the strongest sea clutter energy, and alleviates the problem of target gain loss when the target and sea clutter are within the same beam main lobe.

[0018] The present invention will be further described below with reference to specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing of the present invention.

[0020] Figure 2 Schematic diagram of selecting the Doppler local processing domain in the present invention.

[0021] Figure 3 This is a space-time spectrum diagram before sea clutter suppression in an embodiment of the present invention.

[0022] Figure 4 This is the space-time spectrum after only using the spatial domain singular value decomposition suppression method.

[0023] Figure 5 This is the space-time spectrum after all steps of this method are executed to suppress sea clutter.

[0024] Figure 6 The figure is a comparison chart of the signal-to-clutter ratio improvement after using three methods to suppress sea clutter in an example of the present invention. DETAILED DESCRIPTION

[0025] Example

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0027] As used in this application and the claims, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0028] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to actual proportional relationships. The techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values ​​should be interpreted as being merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, and therefore, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0029] Combine Figure 1 A high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing is characterized by comprising the following steps:

[0030] Step 1: Perform digital beamforming on the range element echo to be processed to obtain the space-time spectrum and determine the frequency distribution range Ω of positive and negative first-order sea clutter:

[0031] If the number of array elements is M and the number of pulses is N, then the space-slow time domain data of a certain range element is an M×N dimensional data matrix X=[x1,x2,…,x M ] T , where the N-dimensional snapshot data of array element m is:

[0032] x m =[x m1 ,x m2 ,…,x mN ]

[0033] Step 2: Select the neighboring range metadata of the current range element as the clutter sample, multiply the space-time slow-time snapshot data received by the radar with the conversion matrix to construct the Doppler local processing domain at a specific frequency.

[0034] Step 2-1, construct the transformation matrix D:

[0035] D=[v t (f0-Δf),v t (f0),v t (f0+Δf)]

[0036] v t (f0)=[1,exp(j2πf0),…,exp(j2π(N-1)f0)] T

[0037] Where f0 is the Doppler frequency, Δf represents the Doppler resolution, and v t (f0) is the time steering vector at Doppler frequency f0;

[0038] Step 2-2: Convert the time steering vector and the echo data of the array element m corresponding to the range unit to be processed into the local processing domain through the conversion matrix:

[0039]

[0040] v0=D H v t (f0)

[0041] Step 3: Estimate the clutter covariance matrix of the Doppler local domain of multiple adjacent range elements, sum them up and average them to obtain the estimated clutter covariance matrix of the range element to be processed R n :

[0042] Step 3-1: Calculate the clutter covariance matrix of the Doppler local domain for the K range elements on both sides of the range element to be processed:

[0043]

[0044] Step 3-2: Sum and average the clutter covariance matrices of the K range elements on both sides of the range element to be processed to obtain an estimate of the clutter covariance matrix of the range element to be processed:

[0045]

[0046] Where d is the number of Doppler units contained in the LPR.

[0047] Step 4: Calculate the optimal weight vector and obtain the signal output by the array element at a specific Doppler frequency after filtering. Traverse all Doppler frequencies of interest to obtain the output signal of sea clutter suppression in the entire Doppler domain:

[0048] Step 4-1: Calculate the optimal adaptive weight vector based on the clutter covariance matrix estimated in step 3:

[0049]

[0050] Step 4-2: Determine the modified sample matrix inverse statistics based on the optimal adaptive weight vector:

[0051]

[0052] Based on this, the output signal y of array element m after sea clutter suppression at Doppler frequency f0 is obtained. m (f0):

[0053]

[0054] Step 4-3: traverse the frequency distribution range Ω of the positive and negative first-order sea clutter determined in step 1 to obtain the output signal after sea clutter suppression in the entire Doppler domain.

[0055] Step 5: Perform singular value decomposition on the clutter samples processed in the time domain to construct the sea clutter subspace and the noise subspace. Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to suppress the sea clutter and obtain the output Y after sea clutter suppression:

[0056] Step 5-1: Assume that the covariance matrix C of the clutter sample array element output data vector y after time domain processing in step 4 is C=yy H , perform singular value decomposition on the covariance matrix C of the array receiving data of the processing distance element:

[0057] [U,S,V]=SVD[C]

[0058] Among them, U represents the left singular matrix, S represents the diagonal matrix composed of singular values, and V represents the right singular matrix;

[0059] Since the sea clutter energy is dominant in the echo signal, the subspace formed by the eigenvectors corresponding to large singular values ​​is the sea clutter subspace U1, and the subspace formed by the eigenvectors corresponding to small singular values ​​is the noise subspace U2. Due to the orthogonality of the noise subspace and the sea clutter subspace, U1 and U2 are orthogonal.

[0060] Step 5-2: Construct the orthogonal projection matrix of the sea clutter subspace:

[0061]

[0062] Among them, I M is a unit array;

[0063] Step 5-3: Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to obtain the output after sea clutter suppression:

[0064] Y=P M y

[0065] Step 6: Use the scan vector to scan all angle ranges to obtain azimuth information, and cascade the spatial singular value decomposition and Doppler domain processing to obtain the array output after cascade processing, which is the output after suppressing sea clutter:

[0066] Use the array steering vector a to scan all angle ranges (radar sector angle range) to obtain azimuth information, and then cascade the spatial singular value decomposition with the Doppler domain to obtain the array output after cascade processing. This output is the output after suppressing sea clutter:

[0067] Y(f0)=a H P M y(f0)

[0068]

[0069] Where a represents the spatial guidance vector, θ i The direction of the incoming wave.

[0070] Step 7: Traverse all frequency points in the clutter area and scan the angle range of interest. Finally, restore the space-time data after clutter suppression to space-slow time data:

[0071] All frequency points in the clutter area are traversed, and the angle range of interest is scanned. Finally, the space-time data Y after clutter suppression is output to obtain the processed space-time spectrum. At this point, all steps of this algorithm are completed.

[0072] In one embodiment, the present invention is further verified in combination with specific examples, and several existing methods are selected for comparison, which shows that the present method has a better signal-to-noise ratio improvement.

[0073] This example uses measured clutter data plus a simulated target. The array elements are 8, the pulse count is 600, the Doppler local domain size is 1×3, the simulated target bearing is 70°, and the Doppler frequency is -0.41 Hz. The space-time spectrum obtained in step 1 shows that the Doppler range in the negative sea clutter region is -0.45 Hz to -0.35 Hz, while the Doppler range in the positive sea clutter region is 0.27 Hz to 0.37 Hz. The simulated target is submerged in the negative sea clutter region, making it difficult to detect and distinguish. Figure 4 The space-time spectrum after using only the spatial domain singular value decomposition sea clutter suppression method is shown. Figure 5 The figure shows the space-time spectrum after all steps of the proposed method are executed and the sea clutter is suppressed. The target is circled by the white ellipse in the figure. The target's azimuth and Doppler information are consistent with the settings, which proves the effectiveness of the algorithm.

[0074] Figure 6 Shown are comparisons of the Doppler spectra at the target angle before and after sea clutter suppression. Two existing methods were used for comparison. Method 1 represents the high-frequency ground-to-sea wave radar sea clutter suppression method based on space-time domain cascade processing, as described in this invention; Method 2 represents the orthogonal projection-time domain cascade method; and Method 3 represents the oblique projection-time domain cascade method. From the perspective of clutter suppression, the method described in this invention not only retains the target, but also suppresses clutter in both positive and negative clutter zones by over 13dB. Compared to the other two methods, this method has no requirements for target orientation and significantly improves the signal-to-clutter ratio.

[0075] In summary, compared with traditional sea clutter suppression methods, the present invention targets the different distribution characteristics of targets and sea clutter echoes received by high-frequency ground-to-air wave radar in the space-time domain, utilizes a space-time cascade method, performs sea clutter suppression in the Doppler local processing domain, and then performs spatial singular value decomposition to further suppress sea clutter. The sea clutter suppression method is effective, feasible, reliable, and has a higher signal-to-clutter ratio.

[0076] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0077] Step 1: Perform digital beamforming on the range element echo to be processed to obtain the space-time spectrum and determine the frequency distribution range of positive and negative first-order sea clutter;

[0078] Step 2: Select the neighboring range metadata of the current range element as the clutter sample, multiply the space-time slow-time snapshot data received by the radar with the conversion matrix to construct the Doppler local processing domain at a specific frequency.

[0079] Step 3: Estimate the clutter covariance matrix of the Doppler local domain of multiple adjacent range elements, sum them up and average them to obtain the estimated clutter covariance matrix of the range element to be processed R n ;

[0080] Step 4: Calculate the optimal weight vector and obtain the signal output by the array element at a specific Doppler frequency after filtering; traverse all Doppler frequencies of interest to obtain the output signal of sea clutter suppression in the entire Doppler domain;

[0081] Step 5: Perform singular value decomposition on the clutter samples processed in the time domain to construct a sea clutter subspace and a noise subspace. Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to suppress the sea clutter and obtain the output Y after sea clutter suppression.

[0082] Step 6: Use the scan vector to scan all angle ranges to obtain azimuth information, and cascade the spatial singular value decomposition and Doppler domain processing to obtain the array output after cascade processing, which is the output after suppressing sea clutter.

[0083] Step 7: Traverse all frequency points in the clutter area and scan the angle range of interest. Finally, restore the space-time data after clutter suppression to space-slow time data.

[0084] A computer storable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program performs the following steps:

[0085] Step 1: Perform digital beamforming on the range element echo to be processed to obtain the space-time spectrum and determine the frequency distribution range of positive and negative first-order sea clutter;

[0086] Step 2: Select the neighboring range metadata of the current range element as the clutter sample, multiply the space-time slow-time snapshot data received by the radar with the conversion matrix to construct the Doppler local processing domain at a specific frequency.

[0087] Step 3: Estimate the clutter covariance matrix of the Doppler local domain of multiple adjacent range elements, sum them up and average them to obtain the estimated clutter covariance matrix of the range element to be processed R n ;

[0088] Step 4: Calculate the optimal weight vector and obtain the signal output by the array element at a specific Doppler frequency after filtering; traverse all Doppler frequencies of interest to obtain the output signal of sea clutter suppression in the entire Doppler domain;

[0089] Step 5: Perform singular value decomposition on the clutter samples processed in the time domain to construct a sea clutter subspace and a noise subspace. Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to suppress the sea clutter and obtain the output Y after sea clutter suppression.

[0090] Step 6: Use the scan vector to scan all angle ranges to obtain azimuth information, and cascade the spatial singular value decomposition and Doppler domain processing to obtain the array output after cascade processing, which is the output after suppressing sea clutter.

[0091] Step 7: Traverse all frequency points in the clutter area and scan the angle range of interest. Finally, restore the space-time data after clutter suppression to space-slow time data.

[0092] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for suppressing sea clutter in high-frequency ground-to-air wave radar based on space-time domain cascade processing, characterized in that: The following steps are involved: Step 1: Perform digital beamforming on the range element echo to be processed to obtain the space-time spectrum and determine the frequency distribution range of positive and negative first-order sea clutter: If the number of array elements is , the number of pulses is , then the space-slow time domain data of a certain distance element is dimensional data matrix , where the array element of The snapshot data is: ； Step 2: Select the neighboring range metadata of the current range element as the clutter sample, multiply the space-time slow-time snapshot data received by the radar with the conversion matrix to construct the Doppler local processing domain at a specific frequency. ; Step 3: Estimate the clutter covariance matrix of the Doppler local domain of multiple adjacent range elements, sum them up and average them to obtain the estimate of the clutter covariance matrix of the range element to be processed. : Step 3-1: For the two sides of the distance element to be processed The clutter covariance matrix of the Doppler local domain is obtained using the range elements: ； Step 3-2: For the two sides of the distance element to be processed The clutter covariance matrix of the range elements is summed and averaged to obtain the estimate of the clutter covariance matrix of the range element to be processed: ； in, is the number of Doppler units contained in the LPR; Step 4: Calculate the optimal weight vector and obtain the signal output by the array element at a specific Doppler frequency after filtering. Traverse all Doppler frequencies of interest to obtain the output signal of sea clutter suppression in the entire Doppler domain: Step 4-1: Calculate the optimal adaptive weight vector based on the clutter covariance matrix estimated in step 3: ； Step 4-2: Determine the modified sample matrix inverse statistics based on the optimal adaptive weight vector: ； And based on this, get the array element At Doppler frequency Output signal after sea clutter suppression : ； Step 4-3: traverse the frequency distribution range of the positive and negative first-order sea clutter determined in step 1 to obtain the output signal after sea clutter suppression in the entire Doppler domain; Step 5: Perform singular value decomposition on the clutter samples processed in the time domain to construct the sea clutter subspace and noise subspace, orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to suppress the sea clutter, and obtain the output after sea clutter suppression. ; Step 6: Use the scan vector to scan all angle ranges to obtain azimuth information, and cascade the spatial singular value decomposition and Doppler domain processing to obtain the array output after cascade processing, which is the output after suppressing sea clutter. Step 7: Traverse all frequency points in the clutter area and scan the angle range of interest. Finally, restore the space-time data after clutter suppression to space-slow time data.

2. The high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing according to claim 1 is characterized in that: The construction of the Doppler local processing domain at a specific frequency in step 2 is specifically as follows: Step 2-1. Construct the transformation matrix : ； ； in, is the Doppler frequency, represents the Doppler resolution, is the Doppler frequency The time-oriented vector under Step 2-2: Use the conversion matrix to convert the time-directed vector and the distance unit to be processed to the corresponding array element The echo data is converted into the local processing domain: ； 。 3. The high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing according to claim 1 is characterized in that: The output after obtaining sea clutter suppression in step 5 , specifically: Step 5-1: Assume the covariance matrix of the clutter sample array element output data vector y after time domain processing in step 4 is , the covariance matrix of the array receiving data of the distance element to be processed Perform singular value decomposition: ； in, represents a left singular matrix, represents the diagonal matrix consisting of singular values, represents a right singular matrix; Since the sea clutter energy is dominant in the echo signal, the subspace composed of the eigenvectors corresponding to large singular values ​​is the sea clutter subspace. , the subspace composed of eigenvectors corresponding to small singular values ​​is the noise subspace , due to the orthogonality of the noise subspace and the sea clutter subspace, and are orthogonal; Step 5-2: Construct the orthogonal projection matrix of the sea clutter subspace: ； in, is a unit array; Step 5-3: Orthogonally project the array data of the specific Doppler frequency point of the range element to be processed into the sea clutter subspace to obtain the output after sea clutter suppression: 。 4. The high-frequency sky-ground wave radar sea clutter suppression method based on space-time domain cascade processing according to claim 3 is characterized in that: The output after suppressing sea clutter in step 6 is specifically obtained as follows: Steering vectors with arrays Scan all angle ranges to obtain azimuth information, and concatenate the spatial singular value decomposition with the Doppler domain to obtain the array output after cascade processing. This output is the output after suppressing sea clutter: ； ； ； in, represents the spatial steering vector, The direction of the incoming wave.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

6. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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