Capon spectrum decomposition coherent IP core design method and device based on HLS

Abstract

The present invention discloses a Capon spectrum solution coherent IP core design method and device based on HLS. The correlation matrix of each sub-array and the whole array is obtained through the divided sub-arrays to reflect the weight proportion of each sub-array with respect to the Capon spectrum, so that the weighted result is more accurate. On the basis of ensuring the aperture, the spatial smoothing sampling method is used to reduce the consumption of hardware resources and improve the resolution ability of coherent signals. In specific use, the user can set the preset number of spatial smoothing times according to their own needs to enhance the versatility of the encapsulated IP core. By generating an IP core that can be directly called by FPGA, the portability of the algorithm is improved. The method occupies less hardware resources and has a fast calculation speed, and has strong applicability in engineering.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to a method and a device for designing a Capon spectral solution coherent IP core based on HLS. Background Art

[0002] The Capon algorithm is a signal processing algorithm based on spatial spectrum estimation, which is mainly used for signal source location and interference suppression. The core idea of ​​the algorithm is to estimate the spatial spectrum of the received signal to obtain the spatial position information of the signal, so as to achieve the purpose of locating the signal source and suppressing interference. In engineering, the Capon algorithm first pre-processes the received signal, including noise removal, filtering and other operations, and then uses the signal received by the array antenna to construct the covariance matrix, and then obtains the spatial spectrum estimation of the signal source by eigendecomposing the covariance matrix, thereby obtaining the spatial position information of the signal source.

[0003] The Capon algorithm is usually implemented in FPGA using Verilog language with IP cores such as multipliers. However, the process of constructing the covariance matrix and performing spatial spectrum estimation in this implementation method not only consumes a large amount of FPGA resources, but also has the problems of low versatility, poor portability and insufficient algorithm accuracy. More importantly, this implementation method is not widely used for the Capon spectrum solution coherence method. When multiple signal sources are located within a certain distance range, if the distance between some signal sources is too close, they become coherent signals. When calculating the covariance matrix, these coherent signals are merged into one signal, causing the direction vectors of some coherent signals to be non-orthogonal to the noise subspace, and no peaks will appear on the spatial spectrum curve, which will lead to underreporting of spatial spectrum estimation, which is not conducive to engineering applications. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a Capon spectrum solution coherent IP core design method and device based on HLS. The technical problem to be solved by the present invention is achieved by the following technical solutions:

[0005] In a first aspect, the present invention provides a Capon spectral solution coherent IP core design method based on HLS, the method comprising:

[0006] Obtain the number of array elements in the receiving array of the radar system;

[0007] Dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays;

[0008] The correlation matrix between each sub-array and the whole array is obtained through the divided sub-arrays. According to the correlation matrix between each sub-array and the whole array and the spatial angle required for decoherence, the generation algorithm of Capon power spectrum density and Capon diagram is designed.

[0009] The HLS tool is used to encapsulate the generation algorithm of Capon power spectral density and Capon graph to obtain a generalized Capon spectral decomposition coherent IP core.

[0010] In a second aspect, the present invention provides a Capon spectrum solution coherent IP core design device based on HLS, the device comprising:

[0011] An acquisition module, used for acquiring the number of array elements of a receiving array in a radar system;

[0012] A division module, used for dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays;

[0013] A design module is used to obtain a correlation matrix between the sub-array and the whole array through the divided sub-array, and to design a generation algorithm for obtaining Capon power spectrum density and Capon graph according to the correlation matrix between the sub-array and the whole array and the spatial angle required for decoherence;

[0014] The encapsulation module is used to encapsulate the generation algorithm of Capon power spectrum density and Capon graph using the HLS tool to obtain a generalized Capon spectrum decomposition coherent IP core.

[0015] Beneficial effects of the present invention:

[0016] In the solution provided by the present invention, the correlation matrix of each sub-array and the whole array is obtained through the divided sub-arrays to reflect the weight proportion of each sub-array with respect to the Capon spectrum, so that the weighted result is more accurate; on the basis of ensuring the aperture, the spatial smoothing sampling method is used to reduce the consumption of hardware resources and improve the resolution ability of coherent signals; in specific use, the user can set the preset number of spatial smoothing times according to their own needs to enhance the versatility of the encapsulated IP core; by generating an IP core that can be directly called by FPGA, the portability of the algorithm is improved; it occupies less hardware resources, has a fast calculation speed, and has strong applicability in engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for designing a Capon spectrum-decomposition coherent IP core based on HLS provided by an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of a sub-array for smoothing processing in a receiving array provided by an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of the structure of a Capon spectral solution coherent IP core design device based on HLS provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] The embodiment of the present invention provides a Capon spectrum solution coherent IP core design method based on HLS, such as Figure 1 As shown, this may include:

[0022] S1, obtain the number of array elements of the receiving array in the radar system.

[0023] It can be understood that the number of array elements is obtained to support the calculation process of the spatial smoothing sub-matrix corresponding to the subsequent step S2 and the Capon spectrum corresponding to S3.

[0024] S2, dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays.

[0025] For S2, this may include:

[0026] According to the number of array elements and the preset spatial smoothing times Divide the receiving array into sub-matrices, as the divided sub-matrices, each sub-matrix includes array elements; among them, , and The preset first formula is satisfied;

[0027] The first preset formula is as follows:

[0028] .

[0029] For details, see Figure 2 The embodiment of the present invention provides a solution for smoothing subarrays in a receiving array. In the implementation process, the number of smoothing times of a preset space is required. will contain The array of array elements is divided into sub-arrays, each of which has The number of array elements is the preset spatial smoothing times. , these subarrays overlap after translation, , and The preset first formula is satisfied.

[0030] Number of array elements in a single subarray The number of elements in the receiving array must not be less than Preset spatial smoothing times The larger the value, the better the smoothing decorrelation effect. will decrease, The reduction of will increase the aperture loss and reduce the angular resolution. Therefore, when applying, the number of array elements should be considered. Select the appropriate preset spatial smoothing times according to specific requirements , when the requirement for smoothing decorrelation effect is high, You can choose a relatively large value when you need higher accuracy. A relatively large value can be selected, and the specific value can be debugged and confirmed by selecting the corresponding value.

[0031] It can be understood that in S2, a spatial smoothing method is used to obtain sub-matrices to support the calculation of the covariance matrix and the correlation matrix of each sub-matrix in S3.

[0032] S3, obtain the correlation matrix between each sub-array and the whole array through the divided sub-arrays, and design the generation algorithm of Capon power spectrum density and Capon diagram according to the correlation matrix between each sub-array and the whole array and the spatial angle required for decoherence.

[0033] For S3, the execution process of the algorithm for generating Capon power spectrum density and Capon diagram may include:

[0034] S31, based on the data received by each array element in the receiving array, obtaining a covariance matrix corresponding to each sub-array may include:

[0035] The data received by each array element in the receiving array is processed using the second formula to obtain the covariance matrix corresponding to each sub-array; the second formula is as follows:

[0036] ;

[0037] in, Indicates A sub-array, Indicates The covariance matrix of the sub-matrices is, Indicates The sub-array The data received by each array element is express The conjugate transpose of Indicates the preset number of spatial smoothing times. Indicates the number of array elements in each sub-array.

[0038] S32, obtaining the correlation matrix between the sub-array and the whole array corresponding to each sub-array according to the covariance matrix corresponding to each sub-array, thereby obtaining a weighted sub-array covariance matrix, which may include:

[0039] According to the covariance matrix corresponding to each sub-array, the covariance matrix of the entire receiving array is obtained;

[0040] According to the covariance matrix corresponding to each sub-array and the covariance matrix of the whole array, the correlation matrix between the sub-array corresponding to each sub-array and the whole array is obtained;

[0041] For each sub-array, the covariance matrix corresponding to the sub-array and the correlation matrix between the sub-array and the whole array are processed using the third formula to obtain the weighted sub-array covariance matrix corresponding to the sub-array; the third formula is as follows:

[0042] ;

[0043] in, Indicates A sub-array, Indicates The weighted sub-matrix covariance matrix corresponding to the sub-matrices is, Indicates The covariance matrix of the sub-matrices is, No. The correlation matrix between the sub-matrix corresponding to the sub-matrix and the whole matrix, Indicates the preset number of spatial smoothing times.

[0044] Exemplarily, two matrices A and B are set, where A is a matrix of dimension Q×Y, B is a matrix of dimension Q×Z, Q represents the number of elements in each row of matrix A, Y represents the number of elements in each column of matrix A, Z represents the number of elements in each column of matrix B, and the number of elements in each row of matrix B is Q, which is the same as the number of elements in each row of matrix A. The method for obtaining the correlation matrix corresponding to A and B is as follows:

[0045] Calculate the mean vector corresponding to matrices A and B respectively and ;

[0046] Subtract the mean vector from each column to get the mean-free matrix and :

[0047] , ;

[0048] Calculate the matrix separately and The corresponding standard deviation and ;

[0049] The correlation matrix is ​​obtained using the following formula: ;

[0050] .

[0051] It is understandable that the known spatial smoothing method for obtaining sub-arrays cannot take into account both aperture loss and smoothing decorrelation effects. Therefore, after obtaining the covariance matrix of each sub-array, the embodiment of the present invention obtains the covariance matrix of the entire array, and calculates the correlation matrix between the covariance matrix of each sub-array and the covariance matrix of the entire array, thereby obtaining a new weighted sub-array covariance matrix to reflect the weight proportion of each sub-array with respect to the Capon spectrum. The correlation is eliminated by smoothing, and the covariance matrix of the entire array retains the global information, thus ensuring the removal of correlation while reducing aperture loss.

[0052] S33, using a spatial smoothing algorithm to process the weighted sub-array covariance matrix corresponding to each sub-array to obtain the covariance matrix required for calculating the Capon power spectrum density, may include:

[0053] Based on the spatial smoothing algorithm, the weighted sub-array covariance matrix corresponding to each sub-array is processed using the fourth formula to obtain the covariance matrix required for calculating the Capon power spectral density; the fourth formula is as follows:

[0054] ;

[0055] in, Indicates A sub-array, represents the covariance matrix required to calculate the Capon power spectral density, Indicates the preset number of spatial smoothing times. Indicates The weighted sub-matrix covariance matrix corresponding to the sub-matrices.

[0056] S34, obtaining an array response vector corresponding to the spatial angle required for decorrelation, and obtaining a Capon power spectrum density corresponding to the spatial angle required for decorrelation according to the array response vector and a preset optimization objective function, may include:

[0057] S341, obtaining an array response vector corresponding to a spatial angle required for decorrelation.

[0058] The expression of the array response vector corresponding to the spatial angle required for decoherence is as follows:

[0059] ;

[0060] in, Indicates spatial angle The corresponding array response vector, represents the imaginary unit, Indicates the wavelength of the echo signal, represents the spacing between the elements in the receiving array, Indicates the number of array elements in each sub-array, Represents a transpose operation.

[0061] S342, obtaining the Capon power spectrum density corresponding to the spatial angle required for decorrelation according to the preset optimization objective function, may include:

[0062] The Lagrange multiplier method is used to process the array response vector and the covariance matrix required for calculating the Capon power spectrum density to obtain the optimal solution of the constraint problem corresponding to the preset optimization objective function, so that the preset optimization objective function satisfies the constraint conditions, thereby obtaining the Capon power spectrum density corresponding to the spatial angle required for decoherence; wherein,

[0063] The preset optimization objective function is as follows:

[0064] ;

[0065] in, Represents the Capon power spectral density corresponding to the spatial angle required to solve the coherence The minimum value of represents the beamforming weight vector, represents the conjugate transpose of the beamforming weight vector, Represents the covariance matrix required to calculate the Capon power spectral density;

[0066] The constraints are as follows:

[0067] ;

[0068] in, Indicates spatial angle The corresponding array response vector;

[0069] The optimal solution to the constraint problem corresponding to the preset optimization objective function is as follows:

[0070] ;

[0071] in, Indicates spatial angle The corresponding beamforming weight vector, represents the inverse matrix of the covariance matrix required to calculate the Capon power spectral density, Indicates spatial angle The corresponding array response vector, express The conjugate transpose of ;

[0072] The spatial angle in the Capon power spectral density corresponding to the spatial angle required for decoherence The corresponding power is as follows:

[0073] ;

[0074] in, Indicates spatial angle The corresponding power.

[0075] It can be understood that the idea of ​​Capon beamforming is to minimize the noise power while The signal component of keeps the response as 1. The embodiment of the present invention uses this idea to design the corresponding optimization objective function; and uses the relationship between the spatial angle and the signal component to design the corresponding constraint condition. In the preset optimization objective function, the variable is the beamforming weight vector , the Capon power spectral density corresponding to the spatial angle required for decoherence obtained later is the set of powers corresponding to the spatial angles required for decoherence. Therefore, the independent variable in the Capon power spectral density corresponding to the spatial angle required for decoherence becomes the spatial angle. .

[0076] S35, obtaining a Capon map after decorrelation according to the Capon power spectrum density corresponding to the spatial angle required for decorrelation.

[0077] Specifically, the Capon power spectral density corresponding to all the spatial angles required for decorrelation is calculated through step S3, and an image is drawn to obtain a Capon diagram after decorrelation; in the Capon diagram after decorrelation, the horizontal axis is the spatial angle or spatial frequency, and the vertical axis is the power , and then we can get the angular distribution of the signal space, that is, the signal direction.

[0078] S4, use the HLS tool to encapsulate the Capon power spectrum density and Capon graph generation algorithm to obtain a generalized Capon spectrum decomposition coherent IP core.

[0079] Specifically, the HLS tool is used to perform interface synthesis and timing optimization on steps S1-S3, encapsulate the input and output signals, implement standardized interfaces, and generate a generalized Capon spectral solution coherent IP core, which can be called in the project. Among them, for the calculation of the covariance matrix of each sub-array in step S31, the calculation of the covariance matrix can be written as a function body and repeatedly called to reduce FPGA resource consumption and computing time. Considering the preset spatial smoothing times The effect on decorrelation effect and angular resolution is to set the preset spatial smoothing times as an adjustable parameter. When configuring the IP core, the user can directly specify the preset spatial smoothing times. This customization function greatly enhances the versatility of the IP core, enabling it to flexibly adapt to different engineering requirements.

[0080] The generation algorithm of the Capon power spectrum density and the Capon diagram of the embodiment of the present invention is encapsulated in the IP core, and C++ is used to compile and implement it when calling, so the implementation process is simple and easy to understand, and has strong portability. The present invention is implemented by calling the Vivado-HLS tool of Xilinx, and the generation algorithm of the Capon power spectrum density and the Capon diagram can also be implemented using the System Generator tool of Xilinx. The Vivado-HLS tool is used to implement part of the algorithm, and this part of the code is generated into an IP core that can be called by the System Generator tool, and the remaining algorithm part can be implemented by the System Generator tool. In the specific operation, one implementation method is to first develop part of the algorithm module through Vivado-HLS, encapsulate it as an IP core, and import it into the System Generator for calling and integration, and the remaining algorithm is completed in the System Generator environment. This method can combine the efficiency of high-level synthesis with the flexibility of graphical design. Another implementation method is to completely rely on the System Generator tool to implement the entire algorithm process. The tool adopts a graphical design method, does not need to write code, and can complete the construction and verification of complex systems by calling the formula module provided by Xilinx.

[0081] The present invention can be implemented inside the FPGA when calling the IP core. Since the algorithm is packaged, the user only needs to input the number of array elements of the receiving array in the radar system and the spatial angle required for decoherence when calling, and the IP core can be called using the FPGA. The calling IP core execution algorithm can be compiled in C++, so it has the advantages of simple program, high calculation accuracy, and easy to understand. Of course, the present invention can also design a parallel processing program to perform parallel processing on different signal source directions and numbers. Compared with the implementation method of the back-end algorithm, the present invention has a shorter calculation time in the FPGA and reduces the complexity of data interaction.

[0082] The present invention can use the optimization instructions of the Vivado-HLS tool, such as the PIPELINE instruction pipeline design, the LOOP_TRIPCOUNT instruction manually specifying the number of iterations of loop execution, etc. Through these instructions, the program synthesis can be used to perform timing optimization and resource adjustment when the IP core is called, reducing the algorithm execution time and resource consumption.

[0083] In a second aspect, corresponding to the above method embodiment, the embodiment of the present invention further provides a Capon spectrum solution coherent IP core design device based on HLS, such as Figure 3 As shown, this may include:

[0084] An acquisition module, used for acquiring the number of array elements of a receiving array in a radar system;

[0085] A division module, used for dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays;

[0086] A design module is used to obtain a correlation matrix between the sub-array and the whole array through the divided sub-array, and to design a generation algorithm for obtaining Capon power spectrum density and Capon graph according to the correlation matrix between the sub-array and the whole array and the spatial angle required for decoherence;

[0087] The encapsulation module is used to encapsulate the generation algorithm of Capon power spectrum density and Capon graph using the HLS tool to obtain a generalized Capon spectrum decomposition coherent IP core.

[0088] It can be understood that in the Capon spectral solution coherent IP core design device proposed in the embodiment of the present invention, the working principle of the acquisition module please refer to the corresponding introduction of step S1 in the method embodiment, the working principle of the division module please refer to the corresponding introduction of step S2 in the method embodiment, the working principle of the design module please refer to the corresponding introduction of step S3 in the method embodiment, and the working principle of the encapsulation module please refer to the corresponding introduction of step S4 in the method embodiment, and no further details will be given here.

[0089] The embodiment of the present invention obtains the correlation matrix of each sub-array and the whole array through the divided sub-arrays to reflect the weight proportion of each sub-array with respect to the Capon spectrum, so that the weighted result is more accurate; on the basis of ensuring the aperture, the spatial smoothing sampling method is used to reduce the consumption of hardware resources and improve the resolution ability of coherent signals; in specific use, the user can set the preset number of spatial smoothing times according to their own needs to enhance the versatility of the encapsulated IP core; by generating an IP core that can be directly called by FPGA, the portability of the algorithm is improved; it occupies less hardware resources, has a fast calculation speed, and has strong applicability in engineering.

[0090] It should be noted that the execution subject of the HLS-based Capon spectral decomposition coherent IP core design method provided in the embodiment of the present invention can be a HLS-based Capon spectral decomposition coherent IP core design device, and the device can be run in an electronic device. Among them, the electronic device can be a server or a terminal device, of course, but is not limited to this.

[0091] It should be noted that in the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0092] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device 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.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A Capon spectrum solution coherent IP core design method based on HLS, characterized in that: include: Obtain the number of array elements in the receiving array of the radar system; Dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays; The correlation matrix between each sub-array and the whole array is obtained through the divided sub-arrays. According to the correlation matrix between each sub-array and the whole array and the spatial angle required for decoherence, the generation algorithm of Capon power spectrum density and Capon diagram is designed. The HLS tool is used to encapsulate the generation algorithm of Capon power spectral density and Capon graph to obtain a generalized Capon spectral decomposition coherent IP core.

2. The method for designing a Capon spectrum coherent IP core based on HLS according to claim 1, characterized in that: The receiving array is divided according to the number of array elements and the preset number of spatial smoothing times to obtain the divided sub-arrays, including: According to the number of array elements and preset spatial smoothing times Divide the receiving array into sub-matrices, as the divided sub-matrices, each sub-matrix includes array elements; among them, , and The preset first formula is satisfied; The preset first formula is as follows: 。 3. The method for designing a Capon spectrum-based coherent IP core based on HLS according to claim 1, characterized in that: The correlation matrix between each sub-array and the whole array is obtained through the divided sub-arrays, and a generation algorithm of Capon power spectrum density and Capon diagram is designed according to the correlation matrix between each sub-array and the whole array and the spatial angle required for decoherence; The execution process of the Capon power spectrum density and Capon diagram generation algorithm includes: Based on the data received by each array element in the receiving array, a covariance matrix corresponding to each sub-array is obtained; According to the covariance matrix corresponding to each sub-array, the correlation matrix between the sub-array and the whole array corresponding to each sub-array is obtained, so as to obtain the weighted sub-array covariance matrix; The weighted sub-array covariance matrix corresponding to each sub-array is processed using a spatial smoothing algorithm to obtain the covariance matrix required for calculating the Capon power spectral density; Obtaining an array response vector corresponding to a spatial angle required for decorrelation, and obtaining a Capon power spectral density corresponding to the spatial angle required for decorrelation according to the array response vector and a preset optimization objective function; The Capon diagram after decorrelation is obtained according to the Capon power spectrum density corresponding to the spatial angle required for decorrelation.

4. The method for designing a Capon spectrum-based coherent IP core according to claim 3, characterized in that: The method of obtaining a covariance matrix corresponding to each sub-array based on the data received by each array element in the receiving array includes: The data received by each array element in the receiving array is processed using the second formula to obtain the covariance matrix corresponding to each sub-array; the second formula is as follows: ； in, Indicates A sub-array, Indicates The covariance matrix of the sub-matrices is, Indicates The sub-array The data received by each array element is represented by The conjugate transpose of Indicates the preset number of spatial smoothing times. Indicates the number of array elements in each sub-array.

5. The method for designing a Capon spectrum-based coherent IP core based on HLS according to claim 3, characterized in that: The correlation matrix between the sub-array and the whole array corresponding to each sub-array is obtained according to the covariance matrix corresponding to each sub-array, so as to obtain the weighted sub-array covariance matrix, including: According to the covariance matrix corresponding to each sub-array, the covariance matrix of the entire receiving array is obtained; According to the covariance matrix corresponding to each sub-array and the covariance matrix of the whole array, the correlation matrix between the sub-array corresponding to each sub-array and the whole array is obtained; For each sub-array, the covariance matrix corresponding to the sub-array and the correlation matrix between the sub-array and the whole array are processed using the third formula to obtain the weighted sub-array covariance matrix corresponding to the sub-array; the third formula is as follows: ； in, Indicates A sub-array, Indicates The weighted sub-matrix covariance matrix corresponding to the sub-matrices is, Indicates The covariance matrix of the sub-matrices is, Indicates The correlation matrix between the sub-matrix corresponding to the sub-matrix and the whole matrix is: Indicates the preset number of spatial smoothing times.

6. The method for designing a Capon spectrum coherent IP core based on HLS according to claim 3, characterized in that: The method of using a spatial smoothing algorithm to process the weighted sub-array covariance matrix corresponding to each sub-array to obtain the covariance matrix required for calculating the Capon power spectrum density includes: Based on the spatial smoothing algorithm, the weighted sub-array covariance matrix corresponding to each sub-array is processed using the fourth formula to obtain the covariance matrix required for calculating the Capon power spectral density; the fourth formula is as follows: ； in, Indicates A sub-array, represents the covariance matrix required to calculate the Capon power spectral density, Indicates the preset number of spatial smoothing times. Indicates The weighted sub-matrix covariance matrix corresponding to the sub-matrices.

7. The method for designing a Capon spectrum coherent IP core based on HLS according to claim 3, characterized in that: The expression of the array response vector corresponding to the spatial angle required for decorrelation is as follows: ； in, Indicates spatial angle The corresponding array response vector, represents the imaginary unit, Indicates the wavelength of the echo signal, represents the spacing between the elements in the receiving array, Indicates the number of array elements in each sub-array, Represents a transpose operation.

8. The method for designing a Capon spectrum coherent IP core based on HLS according to claim 3, characterized in that: The method of obtaining the Capon power spectrum density corresponding to the spatial angle required for decorrelation according to the preset optimization objective function includes: The Lagrange multiplier method is used to process the array response vector and the covariance matrix required for calculating the Capon power spectrum density to obtain the optimal solution of the constraint problem corresponding to the preset optimization objective function, so that the preset optimization objective function satisfies the constraint conditions, thereby obtaining the Capon power spectrum density corresponding to the spatial angle required for decoherence; wherein, The preset optimization objective function is as follows: ； in, Represents the Capon power spectral density corresponding to the spatial angle required to solve the coherence The minimum value of represents the beamforming weight vector, represents the conjugate transpose of the beamforming weight vector, Represents the covariance matrix required to calculate the Capon power spectral density; The constraints are as follows: ； in, Indicates spatial angle The corresponding array response vector; The optimal solution to the constraint problem corresponding to the preset optimization objective function is as follows: ； in, Indicates spatial angle The corresponding beamforming weight vector, represents the inverse matrix of the covariance matrix required to calculate the Capon power spectral density, Indicates spatial angle The corresponding array response vector, express The conjugate transpose of ; The spatial angle in the Capon power spectral density corresponding to the spatial angle required for decoherence The corresponding power is as follows: ； in, Indicates spatial angle The corresponding power.

9. A Capon spectrum solution coherent IP core design device based on HLS, characterized in that: include: An acquisition module, used for acquiring the number of array elements of a receiving array in a radar system; A division module, used for dividing the receiving array according to the number of array elements and the preset number of spatial smoothing times to obtain divided sub-arrays; A design module is used to obtain a correlation matrix between the sub-array and the whole array through the divided sub-array, and to design a generation algorithm for obtaining Capon power spectrum density and Capon graph according to the correlation matrix between the sub-array and the whole array and the spatial angle required for decoherence; The encapsulation module is used to encapsulate the generation algorithm of Capon power spectrum density and Capon graph using the HLS tool to obtain a generalized Capon spectrum decomposition coherent IP core.

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