CN114859311A - Vivado-HLS-based LCMV sidelobe suppression method - Google Patents
Vivado-HLS-based LCMV sidelobe suppression method Download PDFInfo
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- CN114859311A CN114859311A CN202210345353.XA CN202210345353A CN114859311A CN 114859311 A CN114859311 A CN 114859311A CN 202210345353 A CN202210345353 A CN 202210345353A CN 114859311 A CN114859311 A CN 114859311A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The invention discloses a Vivado-HLS-based LCMV sidelobe suppression method, which comprises the following steps: step 1: acquiring an echo signal data matrix and angle information of an expected target; wherein the angle information comprises a pitch angle and an azimuth angle; step 2: acquiring an autocorrelation matrix and an inverse matrix of the autocorrelation matrix corresponding to the echo signal data matrix based on the echo signal data matrix; and step 3: calculating a static airspace guide vector according to the angle information of the expected target; and 4, step 4: performing dot product processing on the inverse matrix of the autocorrelation matrix and the static airspace guide vector to obtain an optimal weight vector; and 5: generating the optimal weight vector into a target IP core based on Vivado HLS; step 6: and calling the IP core through the FPGA to realize sidelobe suppression. The invention can improve the efficiency, the precision and the portability of sidelobe suppression.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a Vivado-HLS-based LCMV sidelobe suppression method.
Background
With the development of modern radar technology, the interference forms and types of radar are more and more diversified, wherein the suppression of sidelobe interference is a popular research direction of radar anti-interference technology.
In the prior art, adaptive suppression of side lobe interference is generally performed based on a Linear Constrained Minimum Variance (LCMV) beamforming algorithm.
However, in the development process of the prior art, software simulation is usually performed first, and then the algorithm is implemented based on the FPGA. Due to the limitation of verilog language, the operation precision of the algorithm can not be ensured, and the method has the defects of poor portability, long algorithm development period, high time sequence logic adjustment difficulty and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a Vivado-HLS-based LCMV sidelobe suppression method. The technical problem to be solved by the invention is realized by the following technical scheme:
a Vivado-HLS-based LCMV sidelobe suppression method, comprising: step 1: acquiring an echo signal data matrix and angle information of an expected target; wherein the angle information comprises a pitch angle and an azimuth angle; step 2: acquiring an autocorrelation matrix and an inverse matrix of the autocorrelation matrix corresponding to the echo signal data matrix based on the echo signal data matrix; and step 3: calculating a static airspace guide vector according to the angle information of the expected target; and 4, step 4: performing dot product processing on the inverse matrix of the autocorrelation matrix and the static airspace guide vector to obtain an optimal weight vector; and 5: generating the optimal weight vector into a target IP core based on Vivado HLS; step 6: and calling the target IP core through the FPGA to realize sidelobe suppression.
In one embodiment of the present invention, the step 1 comprises: step 1-1: sampling the acquired echo signal data matrix of the baseband; wherein the echo signal data matrix is represented as: the method comprises the following steps that X is [ N × M ], wherein N represents the number of channels, and M represents length information of echo signals after sampling; step 1-2: and expressing the echo signal data matrix after sampling processing as follows:
wherein K represents a decimation factor; step 1-3: expressing the acquired pitch angle of the desired target as
The azimuth of the desired target is denoted as θ.
In one embodiment of the present invention, the step 2 comprises: step 2-1: and setting M/K as Ns to obtain an autocorrelation matrix corresponding to the echo signal data matrix X, wherein the autocorrelation matrix is expressed as:
wherein Ns represents the length of original data after K times of sampling; step 2-2: inverting the autocorrelation matrix to obtain an inverse of the autocorrelation matrix, represented as:
wherein, the sizes of the autocorrelation matrix and the inverse matrix of the autocorrelation matrix are both NxN.
The invention has the beneficial effects that:
1. the method can utilize Vivado HLS tools to compile by adopting C + + language, so that the precision of the operation result is improved, the calculation simplicity is improved, the development efficiency of the algorithm is effectively improved, the IP core which can be directly called by the FPGA is generated, and the transportability of the algorithm is greatly improved;
2. according to the invention, the acquired echo signal data matrix can be sampled firstly, and the data volume can be greatly reduced on the premise of ensuring no distortion of effective data, so that the calculation efficiency of a subsequent autocorrelation matrix and an inverse matrix of the autocorrelation matrix is improved;
3. according to the invention, through Vivado HLS, data are subjected to parallel calculation so as to perform comprehensive optimization or pipeline optimization on the whole code or part of the code, and the like, so that the algorithm efficiency can be improved, and the resource consumption can be reduced.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a schematic diagram of a Vivado-HLS-based LCMV sidelobe suppression method provided by an embodiment of the invention;
fig. 2 is a schematic plan view of an antenna model according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
In the invention, after the target echo signal is subjected to primary signal processing to a baseband, LCMV algorithm processing is carried out on the target echo signal, and finally, sidelobe suppression is carried out while no distortion is generated in the target direction. The invention realizes the anti-interference algorithm by using the C + + language based on Vivado HLS software, finally generates the IP core which can be called by the FPGA, can overcome the defects of low precision, poor transportability and the like caused by verilog language, and can greatly reduce the algorithm development period.
Examples
Referring to fig. 1, fig. 1 is a schematic diagram of a Vivado-HLS-based LCMV sidelobe suppression method according to an embodiment of the present invention, where the method includes:
step 1: acquiring an echo signal data matrix and angle information of an expected target; wherein the angle information comprises a pitch angle and an azimuth angle.
Optionally, step 1 includes:
step 1-1: sampling the acquired echo signal data matrix of the baseband; wherein the echo signal data matrix is represented as:
X＝[N×M]，
wherein, N represents the number of channels, and M represents the length information of the sampled echo signals.
The M points contain effective information such as the velocity and distance of the target. For example, M is 4096, N is 12, and a 12-channel signal is shown.
Step 1-2: and expressing the echo signal data matrix after sampling processing as follows:
where K represents the decimation factor.
The target echo signal data matrix is an NxM dimensional matrix, the receiver is represented to have N receiving channels, the number of sampling points of data of each channel is M, echo data of each channel are sampled by K times, the number of the sampled echo data points is M/K and is represented by Ns, and Ns needs to meet the condition that Ns is more than or equal to 2N.
Since the echo data is usually long, the data matrix occupies large resources and it is inconvenient to calculate the autocorrelation matrix and the inverse matrix of the autocorrelation matrix. In the invention, firstly, the length of the acquired echo signal data is sampled, and the number of the extracted data points is M/K. It should be noted that the number of points obtained by extraction can be set by those skilled in the art according to business needs, as long as the calculation can be simplified and the number of data points can be reduced. For example, since K is 64, the length of the finally obtained data is 64 points, that is, X is [12 × 64 ].
Step 1-3: expressing the acquired pitch angle of the desired target as
The azimuth of the desired target is denoted as θ.
The angle information of the expected target azimuth angle and the pitch angle is provided through the FPGA.
And 2, step: and acquiring an autocorrelation matrix and an inverse matrix of the autocorrelation matrix corresponding to the echo signal data matrix based on the echo signal data matrix.
Optionally, step 2 includes:
step 2-1: and setting M/K as Ns to obtain an autocorrelation matrix corresponding to the echo signal data matrix X, wherein the autocorrelation matrix is expressed as:
where Ns denotes the length of the original data after K times sampling.
Optionally, step 2-1 includes:
step 2-11: and classifying the echo signal data matrix X into N groups of first vectors.
Step 2-12: multiplying the N sets of first vectors by X respectively H To obtain N sets of row vectors. Wherein, X H Representing the conjugate transpose of the data matrix.
Step 2-13: and setting M/K as Ns, and combining the N groups of row vectors and dividing the combined row vectors by Ns to obtain an autocorrelation matrix corresponding to the echo signal data matrix X.
The product of the matrix and the matrix can be optimized by utilizing the property of the matrix from the step 2-11 to the step 2-13 so as to realize high-level simulation and synthesis, and parallel calculation is realized by adding a dataflow instruction, so that the output efficiency of the data stream is greatly improved, the real-time performance is enhanced, and the operation efficiency is improved.
Step 2-2: inverting the autocorrelation matrix to obtain an inverse of the autocorrelation matrix, represented as:
wherein, the sizes of the autocorrelation matrix and the inverse matrix of the autocorrelation matrix are both NxN.
And step 3: and calculating a static airspace guide vector according to the angle information of the expected target.
Optionally, step 3 includes:
step 3-1: calculating a guide vector of each array element to a target direction on a three-dimensional space, and expressing as follows:
wherein, the horizontal direction interval between two adjacent sub-arrays is Dx, and the vertical interval is Dy; λ represents a wavelength;
it should be noted that L (Dx, λ) is a function related to the arrangement of the array element antennas, the array element spacing is related to the radar signal wavelength, and the number and form of the vectors are related to the antenna arrangement and the zero phase position defined by the skilled person. L (Dy, λ) has the same principle.
Step 3-2: a Kronecker product (Kronecker product) is calculated based on ay and az to obtain a static spatial steering vector, expressed as:
ω＝kron(ay,az)。
the number of points for ω is N.
Referring to fig. 2, the antenna in this example shown in fig. 2 comprises 12 elements, i.e. corresponding to 12 channels, each having 16 sub-arrays. Referring to fig. 2, it can be seen that the horizontal distance between two adjacent sub-arrays is Dx, and the vertical distance is Dy. In the present invention, the antenna is 4 × 4, and the center of the entire antenna is set as the null point of the phase. And obtaining static airspace guiding vectors corresponding to the 16 sub-arrays.
In the 12-path channel in fig. 2, the submatrices of the leftmost, rightmost, and the rightmost, of the 16 array elements are not included, so ω is extracted to have the 2 nd, 3 th, 5 th, 6 th, 7 th, 8 th, 9 th, 10 th, 11 th, 12 th, 14 th, and 15 th elements, and these points are in one-to-one correspondence with the submatrices 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 in fig. 2. The number of channels and the arrangement sequence of the subarrays in the example are special examples, and for other examples, the antenna array element arrangement of the corresponding example is matched with the number of the effective channels of the signals, and corresponding effective elements in the guide vector are extracted independently to be combined into a new guide vector.
And 4, step 4: and performing dot multiplication on the inverse matrix of the autocorrelation matrix and the static airspace guide vector to obtain an optimal weight vector.
That is, the optimal weight vector ω based on a Linear Constrained Minimum Variance (LCMV) technique opt 。
The step 4 is represented as:
optionally, the step 4 includes:
step 4-1: the inverse of the autocorrelation matrix is divided into N sets of second vectors by rows.
Step 4-2: and multiplying the N groups of second vectors by static space domain steering vectors respectively to obtain N numerical values.
Step 4-3: and combining the N values to obtain an optimal weight vector.
And 4-1 to 4-3, the product of the matrix and the vector can be optimized by using the property of the matrix to realize high-level simulation and synthesis, and parallel computation is realized by adding a dataflow instruction, so that the output efficiency of the data stream is greatly improved, the real-time performance is enhanced, and the operation efficiency is improved.
In addition, the method can perform synchronous operation optimization on a single for loop in a program by using pipeline instructions or perform MERGE loop optimization on a plurality of non-related for loops by using MERGE optimization based on the Vivado HLS, can reduce the latency of the loop to a certain extent, and effectively reduces the resource consumption. And performing imperfect loop nest optimization on the middle layer by using the for loop of the nested layer. I.e. to optimize the for loop.
And 5: and generating the optimal weight vector as a target IP core based on the Vivado HLS.
It should be noted that the target IP core refers to an IP core that can be directly called by the FPGA.
The invention can reduce the resource occupation, enhance the transportability and reduce the repeated labor of developers by generating the IP core which can be directly called by the FPGA.
Step 6: and calling the target IP core through the FPGA to realize sidelobe suppression.
In conclusion, the invention has the beneficial effects that:
1. the method can utilize Vivado HLS tools to compile by adopting C + + language, so that the precision of the operation result is improved, the calculation simplicity is improved, the development efficiency of the algorithm is effectively improved, the IP core which can be directly called by the FPGA is generated, and the transportability of the algorithm is greatly improved;
2. according to the invention, the acquired echo signal data matrix can be sampled firstly, and the data volume can be greatly reduced on the premise of ensuring no distortion of effective data, so that the calculation efficiency of a subsequent autocorrelation matrix and an inverse matrix of the autocorrelation matrix is improved;
3. according to the invention, through Vivado HLS, data are subjected to parallel calculation so as to perform comprehensive optimization or pipeline optimization on the whole code or part of the code, and the like, so that the algorithm efficiency can be improved, and the resource consumption can be reduced.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (7)
1. A Vivado-HLS-based LCMV sidelobe suppression method is characterized by comprising the following steps:
step 1: acquiring an echo signal data matrix and angle information of an expected target; wherein the angle information comprises a pitch angle and an azimuth angle;
step 2: acquiring an autocorrelation matrix and an inverse matrix of the autocorrelation matrix corresponding to the echo signal data matrix based on the echo signal data matrix;
and step 3: calculating a static airspace guide vector according to the angle information of the expected target;
and 4, step 4: performing dot product processing on the inverse matrix of the autocorrelation matrix and the static airspace guide vector to obtain an optimal weight vector;
and 5: generating the optimal weight vector into a target IP core based on Vivado HLS;
step 6: and calling the target IP core through the FPGA to realize sidelobe suppression.
2. The method of claim 1, wherein step 1 comprises:
step 1-1: sampling the acquired echo signal data matrix of the baseband; wherein the echo signal data matrix is represented as:
X＝[N×M]，
wherein, N represents the number of channels, and M represents the length information of the sampled echo signals;
step 1-2: and expressing the echo signal data matrix after sampling processing as follows:
wherein K represents a decimation factor;
step 1-3: expressing the acquired pitch angle of the desired target as
The azimuth of the desired target is denoted θ.
3. The method of claim 2, wherein step 2 comprises:
step 2-1: and setting M/K as Ns to obtain an autocorrelation matrix corresponding to the echo signal data matrix X, wherein the autocorrelation matrix is expressed as:
wherein Ns represents the length of original data after K times of sampling;
step 2-2: inverting the autocorrelation matrix to obtain an inverse of the autocorrelation matrix, represented as:
wherein, the sizes of the autocorrelation matrix and the inverse matrix of the autocorrelation matrix are both NxN.
4. The method of claim 3, wherein step 3 comprises:
step 3-1: calculating a guide vector of each array element to a target direction on a three-dimensional space, and expressing as follows:
wherein, the horizontal direction interval between two adjacent sub-arrays is Dx, and the vertical interval is Dy; λ represents a wavelength;
step 3-2: computing a kronecker product based on ay and az to obtain a static airspace steering vector, expressed as:
ω＝kron(ay,az)。
5. the method of claim 4, wherein the step 4 comprises:
performing dot product processing on the inverse matrix of the autocorrelation matrix and the static airspace guide vector to obtain an optimal weight vector, which is expressed as:
6. the method of claim 3, wherein the step 2-1 comprises:
step 2-11: classifying the echo signal data matrix X into N groups of first vectors;
step 2-12: multiplying the N groups of first vectors by X respectively H To obtain N sets of row vectors; wherein, X H Representing a conjugate transpose of a data matrix;
step 2-13: and setting M/K as Ns, and combining the N groups of row vectors and dividing the combined row vectors by Ns to obtain an autocorrelation matrix corresponding to the echo signal data matrix X.
7. The method of claim 4, wherein the step 4 comprises:
step 4-1: dividing an inverse matrix of the autocorrelation matrix into N groups of second vectors by rows;
step 4-2: multiplying the N groups of second vectors by static airspace guide vectors respectively to obtain N numerical values;
step 4-3: and combining the N values to obtain an optimal weight vector.
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