Airborne multichannel radar sea clutter suppression method based on prior sea surface information

Abstract

The invention discloses an airborne multi-channel radar sea clutter suppression method based on prior sea surface information. The method comprises the following steps: determining a sample covariance matrix according to radar receiving data; determining a prior clutter noise covariance matrix based on prior sea surface information and radar parameters; estimating a color loading coefficient according to the receiving data of the sample unit to obtain an estimated value of the color loading coefficient; determining a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and the estimated value of the color loading coefficient; and carrying out filtering processing on the radar receiving data based on the space-time filtering weight vector to obtain an echo signal after clutter suppression. The priori clutter noise covariance matrix constructed based on the priori sea surface information is more stable, so that the filter determined by the priori clutter noise covariance matrix still has a better clutter suppression effect under the condition that the number of samples is smaller.

Description

Technical Field

[0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a method for suppressing sea clutter of an airborne multi-channel radar based on prior sea surface information. Background Art

[0002] When an airborne radar is in the downward working mode to observe the ground or the ocean, it will receive a large number of echoes from different directions. At this time, the clutter Doppler is extended and has a large intensity, causing the target to be submerged in the clutter area, which in turn affects the performance of clutter suppression and target detection. The Displaced Phase Center Antenna (DPCA) technology adopts a side-looking antenna layout. By adjusting parameters such as pulse repetition frequency, channel spacing, and airborne platform speed, the two phase centers are overlapped after a specific time interval, thereby eliminating fixed clutter and retaining moving targets. However, this technology cannot effectively suppress sidelobe clutter, and the clutter notch is too wide, which is not conducive to detecting slow targets. In essence, DPCA technology is non-adaptive. The Space-Time Adaptive Processing (STAP) technology proposed later uses the spatial information of multiple channels provided by the multi-receive radar and the time information of the coherent pulse train to perform joint processing in the two-dimensional space and time, and achieves effective clutter suppression with the help of adaptive filtering methods. However, in the background of sea clutter, STAP will suffer serious performance loss and the clutter spectrum will be severely broadened. Moreover, when the number of samples is insufficient, the performance of the space-time two-dimensional filter will also drop significantly, resulting in a decrease in the output signal-to-clutter-plus-noise ratio (SCNR) and an increase in the minimum detectable speed of the target, which will have a serious impact on the system performance.

[0003] In recent years, in order to achieve the robustness of the sample covariance matrix, many methods have focused on reducing the dimension and rank of the covariance matrix and processing it based on prior knowledge. The reduced-dimensional STAP method reduces the system dimension through linear transformations that are independent of the radar echo data, while minimizing the amount of calculation and ensuring good clutter suppression performance and high error robustness. Its core lies in the design of the reduced-dimensional matrix, which has the advantage of being easy to implement in engineering, but the structure is fixed and the adaptability to different non-uniform clutter environments is poor. The reduced-rank STAP method uses echo data to adaptively construct a space-time filter. As long as the system degree of freedom is higher than the number of clutter ranks, clutter can be effectively suppressed. However, in practical applications, due to various errors, the clutter degrees of freedom are difficult to obtain accurately, which affects the performance of the reduced-rank STAP method. The knowledge-assisted STAP method improves the performance of STAP by introducing prior knowledge. Many methods use prior knowledge to construct the clutter covariance matrix, and fuse it with the obtained clutter covariance matrix to form the final covariance matrix. Finally, adaptive weights are generated to achieve clutter suppression, which improves the robustness and performance of STAP, thereby improving the clutter suppression performance of the radar system.

[0004] Although dimensionality reduction and rank reduction through linear transformation reduce the demand for sample units to a certain extent, the generated clutter noise covariance matrix still requires the number of sample units to be greater than the system rank. In addition, in the background of sea clutter, the performance loss of this method is more obvious, resulting in a decrease in the clutter suppression effect, thereby increasing the minimum target detectable speed of the radar system. Summary of the invention

[0005] The embodiment of the present invention provides an airborne multi-channel radar sea clutter suppression method based on prior sea surface information, which can solve the problem that the current clutter suppression method has a poor suppression effect.

[0006] In a first aspect, an embodiment of the present invention provides an airborne multi-channel radar sea clutter suppression method based on prior sea surface information, the method comprising:

[0007] Determine a sample covariance matrix according to radar received data, wherein the radar received data includes received data of all distance units, the distance units include at least a target unit and a sample unit, and the sample unit is around the target unit;

[0008] Determine the prior clutter noise covariance matrix based on the prior sea surface information and radar parameters;

[0009] estimating a color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient;

[0010] Determining a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and an estimated value of the color loading coefficient;

[0011] The radar received data is filtered based on the space-time filtering weight vector to obtain the echo signal after clutter suppression.

[0012] In a second aspect, an embodiment of the present invention provides an airborne multi-channel radar sea clutter suppression device based on prior sea surface information, comprising:

[0013] A first processing unit, the first processing unit is used to determine a sample covariance matrix according to radar received data, wherein the radar received data includes received data of all distance units, the distance units include at least a target unit and a sample unit, and the sample unit is around the target unit;

[0014] A second processing unit, the second processing unit is used to determine a priori clutter noise covariance matrix based on priori sea surface information and radar parameters;

[0015] a third processing unit, the third processing unit being configured to estimate a color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient;

[0016] a fourth processing unit, the fourth processing unit being used to determine a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and an estimated value of the color loading coefficient;

[0017] The filter is used to filter the radar received data based on the space-time filtering weight vector to obtain the echo signal after clutter suppression.

[0018] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store computer programs; the processor can be used to execute the computer program (instructions) stored in the memory to implement the method of the first aspect above.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed, the method of the first aspect described above can be implemented.

[0020] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: according to the method provided by the present invention, a priori clutter noise covariance matrix can be robustly estimated through prior sea surface information, thereby improving the clutter suppression effect when insufficient training samples are available under the sea clutter background; by fusing the sample covariance matrix and the color loading coefficient to determine the space-time filtering weight vector, the output signal-to-noise ratio of the filter can be improved when the number of uniform samples is small, thereby further enhancing the clutter suppression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of an implementation method of an airborne multi-channel radar sea clutter suppression method based on prior sea surface information provided by an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of the structure of an airborne multi-channel radar sea clutter suppression device based on prior sea surface information provided by an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of a range-Doppler spectrum comparison provided by an embodiment of the present invention;

[0025] Figure 4 A schematic diagram for comparing filtering output results provided by an embodiment of the present invention;

[0026] Figure 5 A comparison diagram of filtering effects provided by an embodiment of the present invention;

[0027] Figure 6 Another filtering effect comparison diagram provided by an embodiment of the present invention;

[0028] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0030] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0032] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0033] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0035] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0036] The airborne multi-channel radar sea clutter suppression method based on prior sea surface information provided in the embodiment of the present invention can be applied to signal processing devices such as radars, and the embodiment of the present invention does not impose any limitation on the specific type of the signal processing device.

[0037] Figure 1 The flowchart shown is a method for suppressing sea clutter of an airborne multi-channel radar based on prior sea surface information provided by an embodiment of the present invention. As an example but not a limitation, the method may include steps S101-S105, each of which is described below.

[0038] S101, determining a sample covariance matrix according to radar received data.

[0039] In one example, the K pulse echo signals received by N array elements of the radar receiver within a coherent processing time can be fast sampled to obtain a three-dimensional (spatial channel, pulse and range snapshot) data matrix (i.e., the received data of each range unit). Then, the covariance matrix of the radar received data is sampled and inverted to obtain a sample covariance matrix.

[0040] Specifically, the echo signal received by the radar includes information of the target unit and the sample unit. The target unit is the distance unit where the target signal may be located, and the sample unit is the distance unit around the target unit. Exemplarily, the received data Xl of the lth distance unit can be represented by a column vector as:

[0041] Xl=[x1,1,x2,1,...,xN,1,...xN,K]T

[0042] Wherein, xi,j represents the echo signal corresponding to the j-th pulse of the i-th array element, i is a positive integer less than or equal to N, and j is a positive integer less than or equal to K.

[0043] Exemplarily, the sample covariance matrix may satisfy the following formula:

[0044]

[0045] in, is the sample covariance matrix, and L is the total number of sample units.

[0046] S102, determining a priori clutter noise covariance matrix based on priori sea surface information and radar parameters.

[0047] In one possible implementation, the a priori clutter noise covariance matrix may be constructed from a tapered space-time clutter model.

[0048] Exemplarily, the prior clutter noise covariance matrix may satisfy the following formula:

[0049]

[0050] Where R0 is the prior clutter noise covariance matrix, Tc is the complex amplitude of the kth scattering unit, Tc is determined by the a priori known wind speed, ⊙ represents the Kronecker product, Nc is the total number of scattering units, αk represents Tc is the subspace leakage caused by the sea clutter motion, is the space-time steering vector of the kth scattering unit, δ2 is the constant constraining the noise output power, and Ι is the unit matrix.

[0051] in:

[0052] Tc=Toeplitz(ρI(0),ρI(1),...,ρI(K-1))

[0053] Toeplitz means Tc is the Toeplitz matrix, ρI(·) represents the correlation coefficient, satisfying:

[0054]

[0055] Where pt and qt represent the number of pulses, tpq=(pt-qt)·T is the time interval between the pt-th pulse and the qt-th pulse, and τe is the coherence time of the sea clutter.

[0056] In one example, for the Pierson-Moskowitz wave spectrum, the coherence time of the sea clutter may satisfy the following formula:

[0057]

[0058] uwind is the a priori known wind speed, ρs is the range resolution, λ is the radar wavelength, and erf(x) is the error function

[0059] S103, estimating a color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient.

[0060] In a possible implementation, P units can be selected from L number of sample units to obtain a test sample set Γ, and then a first test matrix is ​​constructed according to the received data in the test sample set Γ; then the remaining Q=LP sample units constitute a sample set γ, and a second test matrix is ​​constructed according to the received data in the sample set γ; finally, based on the color loading coefficient optimization model, the color loading coefficient can be estimated according to the first test matrix and the second test matrix to obtain an estimated value of the color loading coefficient.

[0061] In one example, the color loading coefficient optimization model may satisfy the following formula:

[0062]

[0063] in, is the estimated value of the color loading coefficient, α represents the color loading coefficient, is the color loading matrix, is the color loading sample covariance matrix, and Ι is the unit matrix.

[0064] For example, the optimization result can be obtained by linear search based on the color loading coefficient optimization model.

[0065] S104, determining a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and the estimated value of the color loading coefficient.

[0066] In one example, an estimated value of a color loading matrix reflecting clutter correlation may be constructed based on the sample covariance matrix, the prior clutter noise covariance matrix and the estimated value of the color loading coefficient; and then a space-time filtering weight vector may be determined based on the estimated value of the color loading matrix.

[0067] Exemplarily, the estimated value of the color loading matrix may satisfy the following formula:

[0068]

[0069] in, is the estimated value of the color loading matrix, is the estimated value of the color loading coefficient, R0 is the prior clutter noise covariance matrix, is the sample covariance matrix.

[0070] Exemplarily, the space-time filtering weight vector may satisfy the following formula:

[0071]

[0072] Among them, wCL is the space-time filtering weight vector, and v is the space-time steering vector.

[0073] S105, filtering the radar received data based on the space-time filtering weight vector to obtain an echo signal after clutter suppression.

[0074] Exemplarily, the space-time filtering weight vector and the radar receiving data may be multiplied to obtain the echo signal after clutter suppression.

[0075] According to the method provided by the present invention, a priori sea surface information can be used to achieve a robust estimation of the a priori clutter noise covariance matrix, thereby improving the clutter suppression effect when insufficient training samples are present under a sea clutter background; by fusing the sample covariance matrix and the color loading coefficient to determine the space-time filtering weight vector, the output signal-to-noise ratio of the filter can be improved when the number of uniform samples is small, thereby further enhancing the clutter suppression effect.

[0076] Figure 2 The structure diagram of an airborne multi-channel radar sea clutter suppression device based on prior sea surface information provided by an embodiment of the present invention is shown. As an example but not limitation, the device 200 may include a first processing unit 210, a second processing unit 220, a third processing unit 230, a fourth processing unit 240 and a filter 250.

[0077] Exemplarily, the first processing unit 210 is used to determine the sample covariance matrix based on the radar received data, wherein the radar received data includes the received data of all distance units, the distance units include at least the target units and the sample units, and the sample units are around the target units; the second processing unit 220 is used to determine the prior clutter noise covariance matrix based on the prior sea surface information and radar parameters; the third processing unit 230 is used to estimate the color loading coefficient based on the received data of the sample unit to obtain an estimated value of the color loading coefficient; the fourth processing unit 240 is used to determine the space-time filtering weight vector based on the sample covariance matrix, the prior clutter noise covariance matrix and the estimated value of the color loading coefficient; the filter 250 is used to filter the radar received data based on the space-time filtering weight vector to obtain an echo signal after clutter suppression.

[0078] In order to better illustrate the beneficial effects of the present invention, the following simulation experiments were carried out:

[0079] Simulation experiment 1

[0080] Exemplarily, this experiment targets the sea clutter background of level 2 sea conditions, adds a moving target with an intensity of 3dB at the 300-range gate, and compares the clutter suppression effect of the method provided by the present invention with that of the traditional STAP method (i.e., SMI-STAP method) of the space-time two-dimensional filter based on sampling covariance matrix inversion (Sampling Matrix Inversion, SMI) when there are fewer training samples.

[0081] Figure 3 Shown is a schematic diagram of a comparison of range-Doppler spectrum provided by an embodiment of the present invention.

[0082] For example, Figure 3 (a) is a Range-Doppler spectrum diagram obtained by the method provided by the present invention under the conditions of simulation experiment 1. Figure 3 (b) in FIG. 1 is the range-Doppler spectrum obtained by the traditional SMI-STAP method under the conditions of simulation experiment 1.

[0083] Figure 4 A schematic diagram showing a comparison of filtering output results provided by an embodiment of the present invention is shown.

[0084] Similarly, illustratively, Figure 4 (a) is a filtering output result diagram obtained by the method provided by the present invention under the conditions of simulation experiment 1. Figure 4 (b) in the figure is the filtering output result diagram obtained by the traditional SMI-STAP method under the conditions of simulation experiment 1.

[0085] Simulation experiment 2

[0086] For example, this experiment targets a sea clutter background of level 2 sea conditions, adds a moving target with an intensity of 3dB at a range gate of 350, and compares the clutter suppression effects of the method provided by the present invention with a variety of dimension reduction and rank reduction STAP algorithms when there are fewer training samples.

[0087] Specifically, traditional dimensionality reduction and rank reduction STAP algorithms can include SMI method, factored approach (FA), extended factored approach (EFA), joint domain localization (JDL), space-time multiple beam algorithm (STMB), minimum norm eigencanceler (MNE) and loading sample matrix inversion (LSMI).

[0088] Figure 5 A comparison diagram of filtering effects provided by an embodiment of the present invention is shown.

[0089] For example, Figure 5 (a) is a comparison chart of the improvement factors obtained by the method provided by the present invention and the traditional dimension reduction and rank reduction STAP algorithm under the conditions of simulation experiment 2. Figure 5 (b) is a comparison diagram of the residual power of the original signal after filtering obtained by the method provided by the present invention and the traditional dimension reduction and rank reduction STAP algorithm under the conditions of simulation experiment 2. Among them, Proposed represents the relevant parameter curve of the present invention, and opt represents the relevant parameter curve of the original signal.

[0090] Simulation experiment 3

[0091] Exemplarily, this experiment targets the sea clutter background of level 4 sea conditions, adds a moving target with an intensity of 3dB at a range gate of 350, and compares the clutter suppression effect of the method provided by the present invention with that of various dimensionality and rank reduction STAP algorithms when there are fewer training samples.

[0092] Figure 6 Another filter effect comparison diagram provided by an embodiment of the present invention is shown.

[0093] For example, Figure 6 (a) is a comparison chart of the improvement factors obtained by the method provided by the present invention and the traditional dimension reduction and rank reduction STAP algorithm under the conditions of simulation experiment 3. Figure 6(b) is a comparison diagram of the residual power of the original signal after filtering obtained by the method provided by the present invention and the traditional dimension reduction and rank reduction STAP algorithm under the conditions of simulation experiment 3. Among them, Proposed represents the relevant parameter curve of the present invention, and opt represents the relevant parameter curve of the original signal.

[0094] See also Figure 3-Figure 6 ,Depend on Figure 3-Figure 6 It can be seen that when the number of training samples is small, after clutter suppression is performed by the method provided by the present invention, the difference between the target signal and the remaining clutter signals is more obvious, and the output signal-to-noise ratio is higher, which shows that the method provided by the present invention has a good clutter suppression effect even when the number of training samples is small, and the clutter suppression effect of the present invention is more robust.

[0095] Therefore, according to the method provided by the present invention, a priori sea surface information can be used to achieve a robust estimation of the a priori clutter noise covariance matrix, thereby improving the clutter suppression effect when there are insufficient training samples under the sea clutter background; by fusing the sample covariance matrix and the color loading coefficient to determine the space-time filtering weight vector, the output signal-to-noise ratio of the filter can be improved when the number of uniform samples is small, thereby further enhancing the clutter suppression effect.

[0096] Figure 7 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 7 The electronic device 700 shown may include: at least one processor 710 ( Figure 7 Only one processor is shown in the figure), a memory 720, and a computer program 730 stored in the memory 720 and executable on the at least one processor 710, wherein the processor 710 implements the steps of any of the above-mentioned method embodiments when executing the computer program 730.

[0097] The electronic device 700 may be a processing device such as a robot that can implement the above method. The embodiment of the present invention does not impose any limitation on the specific type of the electronic device.

[0098] Those skilled in the art will understand that Figure 7 The electronic device 700 is merely an example and does not constitute a limitation on the electronic device. The electronic device 700 may include more or fewer components than shown in the figure, or may combine certain components, or may include different components. For example, the electronic device 700 may also include an input and output interface.

[0099] The processor 710 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASTC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0100] The memory 720 may be an internal storage unit in some embodiments, such as a hard disk or a memory. The memory 720 may also be an external storage device in other embodiments, such as a plug-in hard disk, a smart memory card (SmartMemory Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 720 may also include both an internal storage unit and an external storage device. The memory 720 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as the program code of the computer program, etc. The memory 720 may also be used to temporarily store data that has been output or is to be output.

[0101] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0102] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0103] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0104] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A method for suppressing sea clutter of an airborne multi-channel radar based on prior sea surface information, characterized in that: include: Determine a sample covariance matrix according to radar received data, wherein the radar received data includes received data of all distance units, the distance unit includes at least a unit and a sample unit, and the sample unit is around the target unit; Determine the prior clutter noise covariance matrix based on the prior sea surface information and radar parameters; estimating a color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient; Determining a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and an estimated value of the color loading coefficient; The radar received data is filtered based on the space-time filtering weight vector to obtain the echo signal after clutter suppression.

2. The method according to claim 1, characterized in that The priori clutter noise covariance matrix satisfies the following formula: Wherein, R0 is the a priori clutter noise covariance matrix, Tc is the complex amplitude of the kth scattering unit, Tc is determined by the a priori known wind speed, ⊙ represents the Kronecker product, Nc is the total number of scattering units, αk represents Tc is the subspace leakage caused by the sea clutter motion, is the space-time steering vector of the kth scattering unit, δ2 is the constant constraining the noise output power, and Ι is the unit matrix.

3. The method according to claim 2, characterized in that The complex amplitude of the kth scattering element satisfies the following formula: Tc=Toeplitz(ρI(0),ρI(1),...,ρI(K-1)) Where ρI(·) represents the correlation coefficient, and Toeplitz represents that Tc is the Toeplitz matrix.

4. The method according to claim 1, characterized in that: The estimating the color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient includes: Selecting received data of some sample units to construct a first test matrix; constructing a second test matrix according to the received data of the remaining sample units; Based on the color loading coefficient optimization model, the color loading coefficient is estimated according to the first test matrix and the second test matrix to obtain an estimated value of the color loading coefficient.

5. The method according to claim 4, characterized in that The color loading coefficient optimization model satisfies the following formula: in, is the estimated value of the color loading coefficient, α represents the color loading coefficient, is the color loading matrix, is the color loading sample covariance matrix, and Ι is the unit matrix.

6. The method according to claim 1, characterized in that The step of determining a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and the estimated value of the color loading coefficient comprises: Determining an estimated value of a color loading matrix reflecting clutter correlation according to the sample covariance matrix, the prior clutter noise covariance matrix and an estimated value of the color loading coefficient; The space-time filtering weight vector is determined based on the space-time steering vector and the color loading matrix.

7. The method according to claim 6, characterized in that The estimated value of the color loading matrix satisfies the following formula: in, is the estimated value of the color loading matrix, is the estimated value of the color loading coefficient, R0 is the priori clutter noise covariance matrix, is the sample covariance matrix.

8. An airborne multi-channel radar sea clutter suppression device based on prior sea surface information, characterized in that: include: A first processing unit, the first processing unit is used to determine a sample covariance matrix according to radar received data, wherein the radar received data includes received data of all distance units, the distance units include at least a target unit and a sample unit, and the sample unit is around the target unit; A second processing unit, the second processing unit is used to determine a priori clutter noise covariance matrix based on priori sea surface information and radar parameters; a third processing unit, the third processing unit being configured to estimate a color loading coefficient according to the received data of the sample unit to obtain an estimated value of the color loading coefficient; a fourth processing unit, the fourth processing unit being used to determine a space-time filtering weight vector according to the sample covariance matrix, the priori clutter noise covariance matrix and an estimated value of the color loading coefficient; The filter is used to filter the radar received data based on the space-time filtering weight vector to obtain the echo signal after clutter suppression.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the electronic device, the method according to any one of claims 1 to 7 is implemented.

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