Field-programmable gate array implementation method for adaptive beam forming

Abstract

The invention discloses a field-programmable gate array implementation method for adaptive beam forming, which comprises the following steps of: receiving N-channel intermediate frequency sampling data, rearranging the data, and performing fast Fourier transform on pulse number dimensions to obtain a distance-Doppler time-frequency two-dimensional diagram of the N-channel intermediate frequency sampling data; interference monitoring and interference area identification are carried out; extracting interference area sample data and rearranging the data; solving a covariance matrix of the interference area sample data and an inverse matrix of the covariance matrix; solving a self-adaptive beam forming coefficient; and performing sum-difference-difference beam forming. According to the time-frequency spectrum of the channel sample data, the time-frequency sample is extracted only in the area where the interference sample is located by utilizing priori knowledge, so that pollution of clutters and zero-frequency components to the sample is avoided, the extracted interference sample information is purer, and the anti-interference performance of a radar system is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a field programmable gate array implementation method for adaptive beamforming based on time-frequency sample selection. Background Art

[0002] The digital beamforming technology of array antenna is a research progress in the digital development of phased array antenna. It has obvious technical advantages. The system has the characteristics of high reliability, good stability and strong anti-interference ability. Adaptive Digital Beam Forming (ADBF) technology is an advanced array signal processing technology developed in the past two or three decades. It uses software methods to align the main beam of the array with the desired signal and the nulling with the unknown interference source signal by complex weighting the received signals of each element of the array antenna, thereby greatly improving the receiving performance of the system. ADBF has the advantages of fast adaptive beam nulling, ultra-low sidelobe, super resolution, adaptive space-time processing, etc. It has been widely used in many fields such as radar, sonar, wireless communication and radio astronomy.

[0003] In traditional beamforming, there is a situation where the null at the interference location is narrow and the interference power is unstable, resulting in a shallow interference null. Summary of the invention

[0004] The purpose of the present invention is to provide a field programmable gate array (FPGA) implementation method for adaptive beamforming, so as to solve the problems existing in the prior art that the null at the interference location is narrow and the interference power is unstable, resulting in a shallow interference null, which affects the anti-interference performance of the radar system.

[0005] In order to achieve the above objectives, the present invention is implemented by the following technical solutions:

[0006] The present invention provides a field programmable gate array implementation method for adaptive beamforming, comprising:

[0007] Receive N-channel intermediate frequency sampling data, rearrange the data of the N-channel intermediate frequency sampling data, and perform fast Fourier transform on the pulse dimension to obtain a range-Doppler time-frequency two-dimensional graph of the N-channel intermediate frequency sampling data;

[0008] Perform interference monitoring and interference area identification according to the range-Doppler time-frequency two-dimensional diagram;

[0009] Extracting interference area sample data according to the identified interference area, and re-arranging the interference area sample data;

[0010] Obtaining a covariance matrix of the sample data in the interference area and an inverse matrix of the covariance matrix; obtaining adaptive beamforming coefficients;

[0011] Sum-difference beamforming is performed according to the adaptive beamforming coefficients.

[0012] Preferably, the receiving of N-channel intermediate frequency sampling data and the data rearrangement of the N-channel intermediate frequency sampling data include: after the N-channel intermediate frequency sampling data is digitally down-converted, amplitude-phase corrected and low-pass filtered, the data rearrangement of the N-channel intermediate frequency sampling data is performed according to the number of pulses and the number of range gates.

[0013] Preferably, the interference monitoring and interference area identification based on the distance-Doppler time-frequency two-dimensional graph includes: based on the distance-Doppler time-frequency two-dimensional graph, for the data in each range gate, taking K units as units, obtaining the power average of K units, searching the Doppler dimension, and finding the top several areas with the largest power average according to actual needs, and the top several areas with the largest power average are the areas where the interference is located.

[0014] Preferably, the first three regions with the largest average power values ​​are found according to actual needs, and the first three regions with the largest average power values ​​are the regions where the interference is located.

[0015] Preferably, the data rearrangement includes: performing the data rearrangement according to the number of range gates, the number of frequency gates and the number of interference areas.

[0016] Preferably, the extracting of the interference area sample data specifically includes: extracting the interference area sample data through the following matrix:

[0017]

[0018] Among them, a ij ,b ij ,c ij (i=1,2,…,I;j=1,2,…,J) represent the sample data of three interference areas a, b and c respectively; I represents the number of distance gates; J represents the number of frequency gates, X n Represents the interference area sample data of the nth (n=1, 2, …, N) channel.

[0019] Preferably, rearranging the extracted interference area sample data includes: obtaining an N×(I×J)-dimensional two-dimensional matrix for the sample data of a single interference area, and the two-dimensional matrices of the three interference areas a, b, and c are respectively:

[0020]

[0021] Among them, a ijn ,bijn ,c ijn (i=1,2…,I;j=1,2…,J;n=1,2…,N) represent the rearranged sample data of the three interference areas a, b and c respectively.

[0022] Arrange the rearranged sample data of all interference regions, and obtain the expression of the sample data of the interference region after data rearrangement:

[0023] X=[X a X b X c ].

[0024] Preferably, obtaining the covariance matrix of the interference area sample data and the inverse matrix of the covariance matrix includes: obtaining the covariance matrix R of all the interference area sample data as:

[0025]

[0026] in,(·) H It means that the matrix is ​​conjugate transposed, and the interference area sample data X is dimensional matrix, x m is the mth (m=1,2,…,3×(I×J)) column vector of X; The specific form is:

[0027]

[0028] The covariance matrix has a total of N*(N+1) / 2 upper triangular elements, and uses N*(N+1) / 2 complex multiplier IP cores for parallel calculation, data pipeline input, and the accumulator IP core is used to parallel accumulate the results of each calculation. The square root method is used to invert the covariance matrix of the sample data in the interference area.

[0029] Preferably, the step of obtaining the adaptive beamforming coefficients includes:

[0030] According to the position of the planar array element, the weight w of the nth element is obtained n :

[0031] w x =exp(-j2πxcosθsinφ / λ)

[0032] w y =exp(-j2πysinθ / λ)

[0033]

[0034] Where (x, y) is the coordinate of the array element; θ and φ are the azimuth and elevation angles respectively; λ is the signal wavelength; wx 、w y are the x-axis component and y-axis component of the weighted weight respectively; is the Kronecker product operator,

[0035] The expression of adaptive beamforming coefficient w is:

[0036] w=R -1 *w N

[0037] Among them, R -1 Find the inverse matrix of the covariance matrix R for the sample data in the interference area, w N are the conventional beamforming coefficients.

[0038] Preferably, performing sum-difference beamforming according to the adaptive beamforming coefficients includes: setting N adaptive array antennas to receive incoming wave signals from a direction of θ, each array element in the adaptive array antenna corresponds to a receiving channel, and each channel is assigned a weighted value, and summing each weighted output data to obtain an expression of the system filtering output:

[0039]

[0040] Where w = [w1, w2, ..., w N ] T represents the weight vector composed of adaptive beamforming weight coefficients, (·) * It means to conjugate the matrix.

[0041] Preferably, according to the expression of the system filtering output, the sum, difference and difference three beams are calculated in parallel to perform sum, difference and difference beamforming.

[0042] The present invention has at least one of the following technical effects:

[0043] The present invention provides a field programmable gate array implementation method for adaptive beamforming based on time-frequency sample selection, which utilizes a two-dimensional time-frequency graph of sampled data to avoid the influence of clutter and zero-frequency components, fully extracts interference area sample data, and compared with extracting interference area sample data in the time domain, the interference area sample data extracted by the present invention is purer and has better anti-interference performance; fast matrix inversion is performed in FPGA based on the Cholseky matrix decomposition algorithm, and the characteristics of FPGA pipeline design are utilized to further improve the real-time performance of adaptive anti-interference processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flowchart of a field programmable gate array implementation method for adaptive beamforming provided by one embodiment of the present invention;

[0045] Figure 2 A schematic diagram of interference area sample data selection for a field programmable gate array implementation method of adaptive beamforming provided by an embodiment of the present invention;

[0046] Figure 3 A flow chart for calculating the covariance matrix and the inverse matrix of adaptive beamforming interference sample data provided by one embodiment of the present invention;

[0047] Figure 4 A schematic diagram of adaptive beamforming provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following is combined with Figure 1-4 The following is a detailed description of a method for implementing a field programmable gate array for adaptive beamforming proposed in the present invention. The advantages and features of the present invention will become clearer according to the following description. It should be noted that the drawings are in a very simplified form and are not in precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purposes, features and advantages of the present invention more obvious and easy to understand, please refer to the drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no technical substantive significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0049] In view of the problem that the null at the interference location in the existing beamforming is narrow and the interference power is unstable, resulting in a shallow interference null, which affects the anti-interference performance of the radar system.

[0050] Reference Figure 1 The present invention provides a field programmable gate array implementation method for adaptive beamforming, comprising:

[0051] Step S1: receiving N-channel intermediate frequency sampling data, rearranging the N-channel intermediate frequency sampling data, and performing fast Fourier transform (FFT) on the pulse dimension to obtain a range-Doppler time-frequency two-dimensional graph of the N-channel intermediate frequency sampling data.

[0052] The received N-channel intermediate frequency sampling data has M pulses and I range gates. After digital down-conversion, amplitude and phase correction, and low-pass filtering, the N-channel intermediate frequency sampling data is rearranged according to the pulse number and the range gate number, and the pulse number dimension is fast Fourier transformed to obtain the range-Doppler time-frequency two-dimensional graph of the received data. The FFT transformation is completed in the FPGA, using the FFT IP core, and the data in the L range gates are parallelly subjected to M-point FFT, where M is usually an integer power of 2.

[0053] Step S2: According to the range-Doppler time-frequency two-dimensional graph, interference monitoring and interference area identification are performed. Figure 2 As shown, the purpose is to avoid clutter and zero-frequency components and find the area where the interference samples are suppressed. The third-dimensional information of the range-Doppler time-frequency two-dimensional diagram is obtained, namely the power spectrum.

[0054] Specifically, according to the range-Doppler time-frequency two-dimensional graph, the data in each range gate is taken as a unit of K units, and the power average value of K units is obtained, and the Doppler dimension is searched, and the first several areas with the largest power average value are found according to actual needs. The first several areas with the largest power average value are the areas where the interference is located. When implemented in FPGA, multiple range gates are searched in parallel to improve the real-time performance of the algorithm processing. In actual engineering applications, the number of interference areas can be selected according to needs.

[0055] In this embodiment, the first three regions with the largest average power values ​​are found according to actual needs, and the first three regions with the largest average power values ​​are the regions where interference occurs.

[0056] Step S3: Extracting interference area sample data according to the identified interference area, and rearranging the data of the interference area sample data according to the number of range gates, the number of frequency gates and the number of interference areas.

[0057] refer to Figure 2 As shown, the extraction of interference area sample data specifically includes: extracting the interference area sample data through the following matrix,

[0058]

[0059] Among them, a ij ,b ij ,c ij (i=1,2,…,I;j=1,2,…,J) represent the sample data of three interference areas a, b and c respectively; I represents the number of distance gates; J represents the number of frequency gates, X nRepresents the interference area sample data of the nth (n=1, 2, ..., N) channel. In actual engineering applications, the number of interference areas can be adjusted according to actual needs, and the dimensions of each interference area can also be different.

[0060] The data rearrangement of the extracted interference area sample data includes: for the sample data of a single interference area, obtaining an N×(I×J)-dimensional two-dimensional matrix, and the two-dimensional matrices of the three interference areas a, b, and c are respectively:

[0061]

[0062] Among them, a ijn ,b ijn ,c ijn (i=1,2…,I;j=1,2…,J;n=1,2…,N) represent the rearranged sample data of the three interference areas a, b and c respectively.

[0063] Arrange the rearranged sample data of all interference regions, and obtain the expression of the sample data of the interference region after data rearrangement:

[0064] X=[X a X b X c ] (3)

[0065] Step S4: Obtain the covariance matrix of the interference area sample data and the inverse matrix of the covariance matrix, and obtain the adaptive beamforming coefficients.

[0066] refer to Figure 3 As shown, the method of obtaining the covariance matrix of the interference area sample data and the inverse matrix of the covariance matrix includes: the covariance matrix R of all the interference area sample data is obtained as:

[0067]

[0068] in,(·) H It means that the matrix is ​​conjugate transposed, and the interference area sample data X is dimensional matrix, x m is the mth (m=1,2,…,3×(I×J)) column vector of X; The specific form is:

[0069]

[0070] The covariance matrix has N*(N+1) / 2 upper triangular elements. It uses N*(N+1) / 2 complex multiplier IP cores for parallel calculation and data pipeline input. The results of each calculation are accumulated in parallel using the accumulator IP core. Then the covariance matrix of the interference sample is inverted, and the square root method (Cholseky matrix decomposition algorithm) is used to invert the covariance matrix of the sample data in the interference area. The calculation flow chart is referenced Figure 3 As shown in the figure. The algorithm is implemented using a pipeline structure, which can reduce the operation time and improve the calculation efficiency. At the same time, for the calculation of the decomposition matrix L and the inverse matrix S of the decomposition matrix, the lower triangular matrix of the Cholseky matrix decomposition algorithm is divided into blocks and parallelized, which can also reduce the operation time, improve the calculation efficiency, and enhance the real-time performance of the processing.

[0071] Specifically, obtaining the adaptive beamforming coefficients includes:

[0072] According to the position of the planar array element, the weight w of the nth element is obtained n :

[0073]

[0074] Where (x, y) is the coordinate of the array element; θ and φ are the azimuth and elevation angles respectively; λ is the signal wavelength; w x 、w y are the x-axis component and y-axis component of the weighted weight respectively; is the Kronecker product operator,

[0075] The expression of adaptive beamforming coefficient w is:

[0076] w=R -1 *w N (7)

[0077] Among them, R -1 Find the inverse matrix of the covariance matrix R for the sample data in the interference area, w N are the conventional beamforming coefficients.

[0078] Step S5: performing sum-difference beamforming according to the adaptive beamforming coefficients.

[0079] refer to Figure 4 As shown, the sum-difference beamforming according to the adaptive beamforming coefficient includes: setting N adaptive array antennas to receive incoming wave signals from a direction of θ, each array element in the adaptive array antenna corresponds to a receiving channel, and each channel is assigned a weighted value, and each weighted output data is summed to obtain the expression of the system filtering output y(t):

[0080]

[0081] Where w = [w1, w2, ..., w N ] T represents the weight vector composed of adaptive beamforming weight coefficients, (·) * Indicates the conjugate treatment of the matrix, x i (t) is the received data of the i-th (i=1, 2, ..., N) channel.

[0082] According to the expression of the system filtering output, the sum-difference three-beam uses 3N complex multiplier IP cores for parallel calculation to improve the real-time performance of the algorithm.

[0083] In summary, the field programmable gate array implementation method of adaptive beamforming provided in this embodiment, based on the time-frequency spectrum of the channel sample data, uses prior knowledge to extract time-frequency samples only in the area where the interference samples are located, avoiding the contamination of the samples by clutter and zero-frequency components, making the extracted interference sample information purer, thereby improving the anti-interference performance of the radar system. The two-dimensional time-frequency graph of the sampled data is used to avoid the influence of clutter and zero-frequency components, and fully extract the interference area sample data. Compared with the extraction of the interference area sample data in the time domain, the extracted interference area sample data is purer and has better anti-interference performance; fast matrix inversion is performed in the FPGA based on the Cholseky matrix decomposition algorithm, and the characteristics of the FPGA pipeline design are used to further improve the real-time performance of the adaptive anti-interference processing.

[0084] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0085] In the description of the present invention, it should be understood that the terms "center", "height", "thickness", "up", "down", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0086] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0087] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0088] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A method for implementing adaptive beamforming using a field programmable gate array, characterized in that: include: Receive N-channel intermediate frequency sampling data, rearrange the data of the N-channel intermediate frequency sampling data, and perform fast Fourier transform on the pulse dimension to obtain a range-Doppler time-frequency two-dimensional graph of the N-channel intermediate frequency sampling data; Perform interference monitoring and interference area identification according to the range-Doppler time-frequency two-dimensional diagram; Extracting interference area sample data according to the identified interference area, and rearranging the interference area sample data; Obtaining a covariance matrix of the sample data in the interference area and an inverse matrix of the covariance matrix; obtaining adaptive beamforming coefficients; Sum-difference beamforming is performed according to the adaptive beamforming coefficients.

2. The method for implementing adaptive beamforming using a field programmable gate array according to claim 1, wherein: The data rearrangement of the N-channel intermediate frequency sampling data includes: after the N-channel intermediate frequency sampling data is digitally down-converted, amplitude-phase corrected and low-pass filtered, the data rearrangement of the N-channel intermediate frequency sampling data is performed according to the number of pulses and the number of range gates.

3. The method for implementing adaptive beamforming using a field programmable gate array according to claim 1, wherein: The interference monitoring and interference area identification according to the distance-Doppler time-frequency two-dimensional graph includes: according to the distance-Doppler time-frequency two-dimensional graph, for the data in each range gate, taking K units as units, obtaining the power average value of K units, searching the Doppler dimension, and finding the first several areas with the largest power average value according to actual needs, and the first several areas with the largest power average value are the areas where the interference is located.

4. The method for implementing adaptive beamforming using a field programmable gate array according to claim 3, wherein: According to actual needs, the first three areas with the largest average power values ​​are found, and the first three areas with the largest average power values ​​are the areas where interference occurs.

5. The method for implementing adaptive beamforming using a field programmable gate array according to claim 4, characterized in that: The extracting the interference area sample data comprises: extracting the interference area sample data through the following matrix, Among them, a ij ,b ij ,c ij (i=1,2,…,I;j=1,2,…,J) represent the sample data of three interference areas a, b and c respectively; I represents the number of distance gates; J represents the number of frequency gates, X n Represents the interference area sample data of the nth (n=1, 2, …, N) channel.

6. The method for implementing adaptive beamforming using a field programmable gate array according to claim 5, characterized in that: The data rearrangement of the interference area sample data includes: for the sample data of a single interference area, obtaining an N×(I×J)-dimensional two-dimensional matrix, the two-dimensional matrices of the three interference areas a, b, and c are respectively: Among them, a ijn ,b ijn ,c ijn (i=1,2…,I;j=1,2…,J;n=1,2…,N) represent the rearranged sample data of the three interference areas a, b and c respectively. Arrange the rearranged sample data of all interference regions, and obtain the expression of the sample data of the interference region after data rearrangement: X=[X a X b X c ]。 7. The method for implementing adaptive beamforming using a field programmable gate array according to claim 6, wherein: The method of obtaining the covariance matrix of the interference area sample data and the inverse matrix of the covariance matrix includes: obtaining the covariance matrix R of all the interference area sample data as: in,(·) H It means that the matrix is ​​conjugate transposed, and the interference area sample data X is dimensional matrix, x m is the mth (m=1,2,…,3×(I×J)) column vector of X; The specific form is: The covariance matrix has a total of N*(N+1) / 2 upper triangular elements, and uses N*(N+1) / 2 complex multiplier IP cores for parallel calculation, data pipeline input, and the accumulator IP core is used to parallel accumulate the results of each calculation. The square root method is used to invert the covariance matrix of the sample data in the interference area.

8. The method for implementing adaptive beamforming using a field programmable gate array according to claim 7, wherein: The step of obtaining the adaptive beamforming coefficients includes: According to the position of the planar array element, the weight w of the nth element is obtained n : w x =exp(-j2πx cosθsinφ / λ) w y =exp(-j2πy sinθ / λ) Where (x, y) is the coordinate of the array element; θ and φ are the azimuth and elevation angles respectively; λ is the signal wavelength; w x 、w y are the x-axis component and y-axis component of the weighted weight respectively; is the Kronecker product operator, The expression of adaptive beamforming coefficient w is: w=R -1 *w N Among them, R -1 Find the inverse matrix of the covariance matrix R for the sample data in the interference area, w N are the conventional beamforming coefficients.

9. The method for implementing adaptive beamforming using a field programmable gate array according to claim 8, characterized in that: The sum-difference beamforming according to the adaptive beamforming coefficient includes: setting N adaptive array antennas to receive incoming wave signals from a direction of θ, each array element in the adaptive array antenna corresponds to a receiving channel, and each channel is assigned a weighted value, and each weighted output data is summed to obtain the expression of the system filtering output: Where w = [w1, w2, ..., w N ] T represents the weight vector composed of adaptive beamforming weight coefficients, (·) * It means to conjugate the matrix.

10. The method for implementing adaptive beamforming using a field programmable gate array according to claim 9, characterized in that: According to the expression of the system filtering output, the sum, difference and difference three beams are calculated in parallel to perform sum, difference and difference beamforming.

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