Radar signal angle measurement system and method based on spatial spectrum estimation

Abstract

The invention belongs to the technical field of radar parameter measurement, and particularly discloses a radar signal angle measurement system and method based on spatial spectrum estimation. According to the application, the calculation of the spatial spectrum estimation function is concentrated in the FPGA for processing, and the steering vector corresponding to the to-be-searched angle range required by the spatial spectrum estimation function for estimating the angle of the to-be-measured radar signal is sent to the FPGA through the microprocessor, so that the response speed when the spatial spectrum estimation function is calculated is improved; and the real-time performance of measuring the angle of the to-be-measured radar signal based on the spatial spectrum estimation function is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of radar parameter measurement, and more specifically, to a radar signal angle measurement system and method based on spatial spectrum estimation. Background Art

[0002] In radar parameter measurement related technologies, the spatial azimuth parameters of radar signals are often estimated by extracting useful characteristic signals received by antenna arrays arranged in a certain way in space. Traditional passive radars use correlation interferometers, but they have problems such as phase ambiguity and hardware channel inconsistency, poor performance, and inability to measure multiple target wave directions.

[0003] Spatial spectrum estimation (MUSIC) is one of the important research directions of radar signal processing. This technology can greatly improve the angle estimation accuracy, angle resolution and other related parameter estimation accuracy of the system processing bandwidth spatial signals, and has the ability to measure multiple targets and multiple directions of arrival. However, the real-time performance of measuring the angle of radar signals is poor and cannot meet engineering requirements. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a radar signal angle measurement system and method based on spatial spectrum estimation, aiming to solve the problem of poor real-time performance of measuring the angle of radar signals in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present application provides a radar signal angle measurement system based on spatial spectrum estimation, comprising:

[0006] A microprocessor and a field programmable logic gate array FPGA connected to the microprocessor;

[0007] A microprocessor, configured to send a steering vector corresponding to the angle range to be searched to the FPGA;

[0008] The FPGA is used to determine the spatial spectrum estimation function according to the eigenvalue and steering vector of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured, and use the angle when the spatial spectrum estimation function obtains the maximum value as the angle of the radar signal to be measured.

[0009] In some embodiments, an FPGA includes:

[0010] The module for solving the autocorrelation matrix is ​​used to solve the autocorrelation matrix.

[0011] In some embodiments, the FPGA further includes:

[0012] An autocorrelation matrix decomposition module is connected to the autocorrelation matrix solving module, and is used to perform Jacobi rotation on the autocorrelation matrix, adjust the non-diagonal elements in the autocorrelation matrix, and determine the diagonal matrix corresponding to the autocorrelation matrix;

[0013] Based on the diagonal matrix, determine the eigenvalues ​​of the autocorrelation matrix.

[0014] In some embodiments, the FPGA further includes:

[0015] A data acquisition module, used to collect radar signals to be tested;

[0016] A data processing module, connected to the data acquisition module, is used to detect the radar signal to be measured and cache certain snapshot data points;

[0017] The Hilbert filter is connected to the data processing module and is used to perform Hilbert filter transformation on the data points to convert the data points into complex signals.

[0018] In some embodiments, the autocorrelation matrix decomposition module is further used to:

[0019] Determine whether the autocorrelation matrix is ​​the first Jacobi rotation. If so, rotate the autocorrelation matrix around any non-diagonal element in the autocorrelation matrix and adjust any non-diagonal element in the autocorrelation matrix.

[0020] Otherwise, the process is repeated with the non-diagonal element with the largest amplitude in the autocorrelation matrix as the center, rotating the non-diagonal element with the largest amplitude, and adjusting the non-diagonal element with the largest amplitude until the maximum number of repetitions is reached;

[0021] Determine the diagonal matrix based on the autocorrelation matrix after the last execution.

[0022] In some embodiments, the autocorrelation matrix decomposition module is further used to:

[0023] Sort the diagonal elements in the diagonal matrix in descending order and determine the eigenvalues ​​based on the first preset number of diagonal elements.

[0024] In some embodiments, the FPGA further includes:

[0025] A spectrum function module is constructed to construct a spatial spectrum estimation function according to the eigenvector and the steering vector corresponding to the eigenvalue.

[0026] In some embodiments, the spectral function module is constructed to further:

[0027] The spatial spectrum estimation function is constructed based on the following formula:

[0028]

[0029] Where P(θ) is the spatial spectrum estimation function, a(θ) is the steering vector, V u is the feature vector.

[0030] In some embodiments, the data processing module includes:

[0031] A detection pulse module, connected to the data acquisition module, is used to detect the radar signal to be measured;

[0032] The first-in-first-out buffer is connected to the detection pulse module and is used to cache data points of a certain snapshot.

[0033] In a second aspect, the present application provides a radar signal angle measurement method based on spatial spectrum estimation, which is applied to the radar signal angle measurement system based on spatial spectrum estimation described in the first method or any of the embodiments of the first aspect, including:

[0034] The steering vector corresponding to the angle range to be searched is sent to the FPGA via the microprocessor;

[0035] The spatial spectrum estimation function is determined by FPGA according to the eigenvalue and steering vector of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured, and the angle at which the spatial spectrum estimation function reaches the maximum value is used as the angle of the radar signal to be measured.

[0036] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0037] The present application provides a radar signal angle measurement system and method based on spatial spectrum estimation, which improves the response speed when calculating the spatial spectrum estimation function, thereby improving the real-time performance of estimating the angle of the radar signal to be measured based on the spatial spectrum estimation function, by centralizing the calculation of the spatial spectrum estimation function in an FPGA and sending a steering vector corresponding to a search angle range required by the spatial spectrum estimation function for estimating the angle of the radar signal to be measured to the FPGA through a microprocessor. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is one of the structural schematic diagrams of the radar signal angle measurement system based on spatial spectrum estimation provided in the embodiment of the present application;

[0039] Figure 2 This is the second structural schematic diagram of the radar signal angle measurement system based on spatial spectrum estimation provided in an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of the structure of the module for solving the autocorrelation matrix provided in an embodiment of the present application;

[0041] Figure 4It is a schematic diagram of the 4-8 dimensional matrix eigenvalue decomposition process provided by the embodiment of the present application;

[0042] Figure 5 This is one of the schematic diagrams of spatial MUSIC spectrum results of DOA estimation of the radar signal to be measured provided in an embodiment of the present application;

[0043] Figure 6 This is the second schematic diagram of the spatial MUSIC spectrum result of the DOA estimation of the radar signal to be measured provided in the embodiment of the present application;

[0044] Figure 7 is a schematic diagram of the RMSE result of azimuth DOA estimation of two radar signals to be measured provided in an embodiment of the present application;

[0045] Figure 8 1 is a schematic diagram of RMSE results of elevation angle DOA estimation of two radar signals to be measured provided in an embodiment of the present application;

[0046] Fig. 9 It is a flow chart of a radar signal angle measurement method based on spatial spectrum estimation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0049] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0050] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0051] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0052] See also Figure 1An embodiment of the present application provides a radar signal angle measurement system based on spatial spectrum estimation, which may include: a microprocessor 10 and a field programmable gate array FPGA 20 connected to the microprocessor 10.

[0053] The microprocessor 10 is used to send the steering vector corresponding to the angle range to be searched to the FPGA 20;

[0054] FPGA 20 is used to determine the spatial spectrum estimation function according to the eigenvalues ​​and steering vectors of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured, and use the angle when the spatial spectrum estimation function obtains the maximum value as the angle of the radar signal to be measured.

[0055] In the embodiment of the present application, the microprocessor may adopt ARM. The radar signal angle measurement system based on spatial spectrum estimation may adopt ZYNQ UltraSCALE+ series chips. The ZYNQ UltraSCALE+ series chips include a logic PL (i.e., FPGA 20) part and a PS part (ARM). The algorithm problems with the largest amount of calculation are concentrated in the PL part (FPGA 20), and the PS is used for interface control and data interaction, and the steering vector corresponding to the angle range to be searched required by the spatial spectrum estimation function for estimating the angle of the radar signal to be measured is sent to the FPGA 20, so as to improve the response speed when calculating the spatial spectrum estimation function.

[0056] The radar signal to be measured may be a passive radar signal or an active radar signal. The embodiment of the present application is described by taking the radar signal to be measured as a passive radar signal as an example.

[0057] In the embodiment of the present application, the PL part (FPGA20) in the ZYNQ UltraSCALE+ series chip is used to construct the spatial spectrum estimation function, which is specifically implemented as follows:

[0058] After processing the passive radar signal, the FPGA 20 obtains a complex signal corresponding to the passive radar signal and calculates an autocorrelation matrix corresponding to the complex signal. A spatial spectrum estimation function is constructed according to the calculated eigenvalues ​​in the autocorrelation matrix and the steering vector corresponding to the angle range to be searched of the spatial spectrum estimation function sent by the ARM. Specifically, the steering vector can be cached in the FLASH memory in advance, and the stored steering vector is read by the FLASH read-write controller in the ARM, and the read steering vector is sent to the DDR3 read-write controller in the FPGA 20 through the AXI bus.

[0059] FPGA20 brings the steering vectors corresponding to different angles in the angle range to be searched into the constructed spatial spectrum estimation function to obtain the value of the spatial spectrum estimation function, and uses the angle when the spatial spectrum estimation function obtains the maximum value as the angle of the measured passive radar signal. The angle may include the azimuth angle and the pitch angle.

[0060] It should be noted that the spatial spectrum estimation algorithm is a classic super-resolution angle of arrival (DOA) estimation method. It directly processes the covariance matrix of any array output data to obtain a signal subspace corresponding to the signal component and a noise subspace orthogonal to the signal component. The orthogonality of these two subspaces is then used to construct a spatial spectrum function, and the direction of the signal is detected by searching for spectral peaks.

[0061] The embodiment of the present application provides a radar signal angle measurement system based on spatial spectrum estimation. The calculation of the spatial spectrum estimation function is concentrated in the FPGA for processing, and a steering vector corresponding to the angle range to be searched required by the spatial spectrum estimation function for estimating the angle of the radar signal to be measured is sent to the FPGA through a microprocessor. This improves the response speed when calculating the spatial spectrum estimation function, thereby improving the real-time performance of measuring the angle of the radar signal to be measured based on the spatial spectrum estimation function.

[0062] Furthermore, in some embodiments, the FPGA 20 may include:

[0063] A data acquisition module, used to collect radar signals to be tested;

[0064] A data processing module, connected to the data acquisition module, is used to detect the radar signal to be measured and cache certain snapshot data points;

[0065] The Hilbert filter is connected to the data processing module and is used to perform Hilbert filter transformation on the data points to convert the data points into complex signals.

[0066] Please see further Figure 2 The FPGA 20 may include a data acquisition module, a data processing module connected to the data acquisition module, and a Hilbert filter connected to the data processing module.

[0067] In an embodiment of the present application, the data acquisition module can be used to collect the above passive radar signal and send the collected passive radar signal to the data processing module. Exemplarily, the data acquisition module can be an 8-channel 14-bit analog-to-digital signal collector, and the passive radar signal is collected by the 8-channel 14-bit analog-to-digital signal collector (AD collector for short). Exemplarily, the passive radar signal can be an intermediate frequency signal.

[0068] The data processing module detects and caches certain snapshot data points according to the passive radar signal collected by the data collection module. In the embodiment of the present application, 256 data points are cached.

[0069] The Hilbert filter performs a Hilbert filter transformation on the data points of a certain snapshot in the buffer, and converts the data points (ie, real signals) into complex signals.

[0070] Furthermore, in some embodiments, the data processing module may include:

[0071] A detection pulse module, connected to the data acquisition module, is used to detect the radar signal to be measured;

[0072] The first-in-first-out buffer is connected to the detection pulse module and is used to cache data points of a certain snapshot.

[0073] Please continue to see Figure 2 The data processing module may be composed of a detection pulse module connected to the data acquisition module and a first-in-first-out (FIFO) buffer connected to the detection pulse module. The detection pulse module is used to detect the passive radar signal sent by the data acquisition module. The FIFO buffer is used to cache certain snapshot data points.

[0074] Furthermore, in some embodiments, the FPGA 20 may further include:

[0075] The module for solving the autocorrelation matrix is ​​used to solve the autocorrelation matrix.

[0076] Please continue to see Figure 2 The FPGA 20 may include an autocorrelation matrix solving module connected to the Hilbert filter, which may be used to solve the autocorrelation matrix of the complex signal corresponding to the passive radar signal.

[0077] In the embodiment of the present application, the calculation formula of the autocorrelation matrix R is as follows:

[0078]

[0079] Wherein, N represents the number of snapshots of cached data points. In the embodiment of the present application, N is 256, and x(n) is the complex signal corresponding to the data point. When the above-mentioned 8-channel 14-bit analog-to-digital signal collector is used to collect passive radar signals, and N is 256, x(n) is an 8*256-dimensional matrix, and each data point contains an in-phase component (I component) and an orthogonal component (Q component).

[0080] In order to realize the fast calculation of the autocorrelation matrix R, the autocorrelation matrix R is divided into blocks and parallelized to improve the calculation efficiency. Figure 3As shown. Taking the data stream of 8-channel dimension as an example, after the RAM cache of 256 clocks, the data stream is divided into multiple groups and read out simultaneously. Taking 6 groups as an example, each group calculates 6 autocorrelation matrix elements in turn. The first group reads out the data stream of channel 1 to channel 6 in turn, and obtains the autocorrelation matrix parameters r11 to r66 in turn through the complex multiplier and accumulator (MAC) in the complex accumulation module, wherein the complex accumulation module is an FPGA general algorithm IP calculation module. Similarly, the implementation method of the second to sixth groups is the same. In the embodiment of the present application, a total of 6 autocorrelation matrix parameters are obtained by simultaneously calculating through 6 complex accumulation modules, and a total of 36 R matrix elements can be obtained. The remaining elements are conjugate with the diagonal and do not need to be calculated separately.

[0081] The block calculation of the autocorrelation matrix R requires a total of 256*6 system clock cycles plus 6 delay cycles of the complex multiplication module 1 to obtain the correct autocorrelation matrix R. In the ZYNQ UltraSCALE+ chip, the system clock rate is 300M. Calling the FPGA's own complex calculation IP core and MAC accumulation core takes about 5us to obtain the autocorrelation matrix R with a snapshot number of 256.

[0082] Furthermore, in some embodiments, the FPGA 20 may further include:

[0083] An autocorrelation matrix decomposition module is connected to the autocorrelation matrix solving module, and is used to perform Jacobi rotation on the autocorrelation matrix, adjust the non-diagonal elements in the autocorrelation matrix, and determine the diagonal matrix corresponding to the autocorrelation matrix;

[0084] Based on the diagonal matrix, determine the eigenvalues ​​of the autocorrelation matrix.

[0085] The module for solving the autocorrelation matrix may include an autocorrelation matrix decomposition module. The autocorrelation matrix is ​​subjected to Jacobi rotation by the autocorrelation matrix decomposition module, and the non-diagonal elements in the autocorrelation matrix are adjusted to obtain a diagonal matrix corresponding to the autocorrelation matrix. The eigenvalues ​​of the autocorrelation matrix are obtained by the diagonal matrix.

[0086] Considering that the effective receiving channel of the passive radar changes dynamically in 4 to 8 dimensions, it is difficult for a fixed algorithm to adapt to the dynamic changes of the channel. Therefore, the module for solving the autocorrelation matrix in the embodiment of the present application can realize the eigenvalue decomposition of the 4 to 8 dynamic dimensional matrix, and adopts the bilateral Jacobi iteration method to continuously perform Jacobi rotation on the autocorrelation matrix R, so that the autocorrelation matrix R is orthogonally similar to the diagonal matrix, that is:

[0087]

[0088] Where ∑ is an 8*8 diagonal matrix containing only diagonal elements, and the rest of the elements are 0, U is the eigenvector, and P 1~P K are the rotation matrices from the 1st to the Kth rotation, K represents the number of times the autocorrelation matrix R rotates, and the element P in the rotation matrix k (i,j,θ k ), i represents the row number, j represents the column number, k = 1, 2...K, then the diagonal elements in the rotation matrix except P ii and P jj All other non-diagonal elements are 1 except P. ij = -sinθ k or P ji = sinθ k All other elements are 0, θ k is the rotation angle, K=M*(M-1) / 2, M is the channel dimension, in this embodiment, M=8, and each time only the non-diagonal elements in the autocorrelation matrix R are rotated as the center. 1 ~P K Both are 8*8 rotation matrices.

[0089] From the derivation of the above formulas, we can see that the eigenvalue decomposition of the autocorrelation matrix is ​​converted into several small Jacobi matrix rotations. FPGA has the CORDIC function, which can easily complete operations such as rotation, modulus, and phase angle of complex vectors. Therefore, the bilateral Jacobi iteration method is extremely suitable for algorithm acceleration in FPGA.

[0090] Furthermore, in some embodiments, the autocorrelation matrix decomposition module is further used to:

[0091] Determine whether the autocorrelation matrix is ​​the first Jacobi rotation. If so, rotate the autocorrelation matrix around any non-diagonal element in the autocorrelation matrix and adjust any non-diagonal element in the autocorrelation matrix.

[0092] Otherwise, the process is repeated with the non-diagonal element with the largest amplitude in the autocorrelation matrix as the center, rotating the non-diagonal element with the largest amplitude, and adjusting the non-diagonal element with the largest amplitude until the maximum number of repetitions is reached;

[0093] Determine the diagonal matrix based on the autocorrelation matrix after the last execution.

[0094] The Jacobi matrix eigenvalue rotation decomposition method based on the central element rotation provided in the embodiment of the present application is an infinite approximation iterative method, specifically as follows Figure 4 After several iterations of rotation, the diagonal elements of the autocorrelation matrix R tend to be stable. In the embodiment of the present application, the iteration stop can be determined by setting the maximum number of repetitions or by setting the upper limit of the modulus of the maximum element.

[0095] In an embodiment of the present application, taking an 8*8 autocorrelation matrix as an example, it is determined whether the 8*8 autocorrelation matrix is ​​the first Jacobi rotation. If so, a matrix eigenvalue decomposition centered on any non-diagonal element in the 8*8 autocorrelation matrix is ​​performed, and the 8*8 autocorrelation matrix is ​​rotated based on the above rotation matrix and rotation angle, and any non-diagonal element in the 8*8 autocorrelation matrix is ​​adjusted, and the cached rotation result is output.

[0096] If the 8*8 autocorrelation matrix is ​​not the first Jacobi rotation, perform matrix eigenvalue decomposition centered on the non-diagonal element with the largest amplitude in the autocorrelation matrix, rotate the non-diagonal element with the largest amplitude, adjust the non-diagonal element with the largest amplitude based on the above rotation matrix and rotation angle, and output the cached rotation result. Repeat this process until the maximum number of repetitions is reached.

[0097] According to the autocorrelation matrix corresponding to the final result output after the last execution, a diagonal matrix is ​​obtained.

[0098] The embodiment of the present application is different from the traditional Jacobi matrix eigenvalue rotation decomposition method based on the rotation of the center element, in which the rotation center is executed sequentially each time to cover all non-diagonal elements as much as possible. The rotation center element in the embodiment of the present application is the maximum value of the non-diagonal elements in the autocorrelation matrix, and the non-diagonal elements with a value of 0 will not participate in the calculation. Therefore, it is only necessary to design an 8*8 matrix eigenvalue decomposition algorithm, and there is no need to separately design a 7*7, 6*6 or 5*5 autocorrelation matrix eigenvalue decomposition algorithm. If a 7*7, 6*6 or 5*5 matrix eigenvalue decomposition is required, it is only necessary to fill the corresponding redundant elements in the input of the 8*8 autocorrelation matrix with 0.

[0099] From the analysis, we can see that each CORDIC algorithm needs to delay 32 clocks. 8 elements share one CORDIC, which takes 8 clocks. The parallel calculation of the vector modulus and phase angle requires CORDIC, which delays 32 clocks. Considering the 5-level cascade of CORDIC IP core, it takes 7 clocks to complete a parallel maximum solution. Considering the delay of 5 clocks between each level of data storage and simple sequential logic, it takes 32*6+8+7+5 clocks to complete a single element rotation. A complete 8*8 matrix cycle iteration requires traversal of 28 non-diagonal elements. A matrix eigenvalue decomposition requires 3 cycle iterations. Considering the system clock of 200M, the total algorithm delay is 5ns*(32*6+8+7+5)*28*5, which is about 100us, which is only one-tenth of data signal processing such as DSP6678.

[0100] The radar signal angle measurement system based on spatial spectrum estimation provided in the embodiment of the present application is different from the traditional method of rotating the autocorrelation matrix with all non-diagonal elements in the autocorrelation matrix as the rotation center. The embodiment of the present application rotates the autocorrelation matrix with the non-diagonal element with the largest amplitude in the autocorrelation matrix as the center, and the non-diagonal elements with an amplitude of 0 will not be used as the rotation center, thereby improving the efficiency of obtaining the diagonal matrix.

[0101] Furthermore, in some embodiments, the autocorrelation matrix decomposition module may also be used to:

[0102] Sort the diagonal elements in the diagonal matrix in descending order and determine the eigenvalues ​​based on the first preset number of diagonal elements.

[0103] Please continue to see Figure 2 The autocorrelation matrix decomposition module can also be used to sort the diagonal elements in the above-mentioned diagonal matrix in descending order, and use the first preset number of diagonal elements as eigenvalues.

[0104] Furthermore, in some embodiments, the FPGA 20 may further include:

[0105] A spectrum function module is constructed to construct a spatial spectrum estimation function according to the eigenvector and the steering vector corresponding to the eigenvalue.

[0106] Please continue to see Figure 2 The FPGA 20 may further include a spectrum function construction module, which may be used to construct a spatial spectrum estimation function according to the eigenvector corresponding to the above eigenvalue and the steering vector of the angle range to be searched.

[0107] Furthermore, in some embodiments, constructing a spectral function module can also be used to:

[0108] The spatial spectrum estimation function is constructed based on the following formula:

[0109]

[0110] Where P(θ) is the spatial spectrum estimation function, a(θ) is the steering vector, V u is the feature vector.

[0111] Please continue to see Figure 2 , the spectrum function building module can also be used to build a spatial spectrum estimation function P(θ) based on the following formula:

[0112]

[0113] Where a(θ) is the steering vector, and a(θ)={1,e jω1 ,e jω2 ,e jω3 …ejω8} H ,ω 1 ~ω 8 They are the phase information of channels 1 to 8, V u is the eigenvector corresponding to the preset number of eigenvalues. By detecting the eigenvalues, the number of eigenvalues ​​with larger values ​​can be obtained, thereby estimating the number of far-field signals.

[0114] The inverse of the spatial spectrum estimation function P(θ) can be transformed into the minimum value of the denominator, and,

[0115] a(θ) H V u V u H a(θ)=a(θ) H V u (a(θ) H V u ) H =|a(θ) H V u |;

[0116] By calculating the spatial spectrum estimation function P(θ), the calculation delay can be obtained more conveniently. A complex multiplication module delays 6 clocks, and parallel calculation of 8 elements requires a delay of 48 clocks. The modulus of the spatial spectrum estimation function P(θ) can be simplified. Since the modulus of the CORDIC orientation vector requires 30 clocks, the simplified processing method for obtaining the modulus of a complex vector a+bj can be described as:

[0117]

[0118] Among them, M′=max(|a|,|b|), N′=min(|a|,|b|), a is the real part, and b is the imaginary part.

[0119] This method can be easily implemented through shifting and combinatorial logic. Only one clock delay is required, so a spectrum function calculation requires 6*8+8*1=56 clocks. Within the field of view, with 1° as a step, there are 61*61=3721 angle-guided vectors. Considering the extreme case where every angle needs to be involved in the calculation, 56*3721 clocks are required. Taking a 200M system clock as an example, 56*3721*5ns is required, which is about 1ms delay. If L areas are searched in parallel, the delay can be reduced to 1 / L milliseconds.

[0120] Finally, the multi-target MUSIC algorithm verification analysis is carried out, taking the eight-element uniform circular array as an example, with a radius of 0.2m. There are two radar signals to be tested in the far field. The frequency of radar signal 1 to be tested is 3GHz, and the incident angle is (10.2°, 20.7°). The frequency of radar signal 2 to be tested is 3GHz+10MHz, and the incident angle is (10.2°, 15.7°). The number of sampling snapshots of both signals is 512, and the signal-to-noise ratio is 10dB. Figure 5 and Figure 6 The results of the measurement using the passive radar angle measurement system based on spatial spectrum estimation provided by the embodiment of the present application are shown in the figure. It can be seen from the results in the figure that the MUSIC algorithm can be used to complete the DOA estimation of the far-field signal, and the estimation result is accurate and consistent with the simulation preset. Therefore, the MUSIC algorithm can complete the high-precision direction finding work of far-field multi-signals.

[0121] The embodiment of the present application also simulates the relationship between the root mean square error of DOA estimation based on the MUSIC algorithm and the signal-to-noise ratio of the far-field signal. Except for the incremental change of the signal-to-noise ratio, the other simulation parameters remain unchanged. A total of 1000 Monte Carlo simulations are performed. The results are as follows: Figure 7 and Figure 8 As shown in the figure, it can be seen that as the far-field signal noise ratio increases, the root mean square error (RMSE) of the MUSIC algorithm DOA estimation will gradually decrease and eventually reach a convergence state, that is, the RMSE of the DOA estimation will be equal to zero or a constant close to zero. When the signal noise ratio is low, the algorithm also has a high estimation accuracy and can meet the system requirements.

[0122] The implementation of the MUSIC algorithm in the FPGA platform fully utilizes the inherent advantages of FPGA parallel computing and greatly improves the algorithm latency. The analog-to-digital signal acquisition system simulates the sampling rate and completes the signal digital IQ decomposition and caching after passing through the JESD IP core. Considering the interface IP core delay, Hilbert transform and 256-point snapshot accumulation, the original data accumulation takes 1us. From the above analysis, it can be seen that the autocorrelation matrix calculation takes about 5us, the matrix eigenvalue decomposition takes 100us, and the spectrum function calculation in all fields of view takes about 1ms. Considering that the spatial spectrum estimation function result needs to be subjected to extreme value discrimination, two-dimensional filtering, two-dimensional interpolation and some algorithm time / space optimization, the final algorithm delay is about 1.3ms. If the spatial spectrum estimation function angle domain block parallel search is taken into account, the total delay can be controlled within 1ms. The entire algorithm test is performed on a single core of TMS320C6678 (main frequency is 1GHz). If the accuracy is to reach the order of 10°, 18.16ms is required, and if the accuracy is to reach the order of 0.01°, 27.30ms is required. Therefore, the angle measurement delay of the passive radar angle measurement system based on spatial spectrum estimation provided in the embodiment of the present application for the radar signal to be measured is much shorter than that of simply using the DSP algorithm to implement MUSIC.

[0123] The passive radar angle measurement system based on spatial spectrum estimation provided in the embodiment of the present application has the ability to resolve and measure the angle of target radar in a multi-bait environment; has the ability to perform rapid passive radar multi-target angle measurement within 2ms; has 4-8 dimensional dynamic eigenvalue decomposition capability, and can adapt to the dynamic changes of the passive radar effective receiving channel in 4-8 dimensions.

[0124] The radar signal angle measurement method based on spatial spectrum estimation provided by the present invention is described below. The radar signal angle measurement method based on spatial spectrum estimation described below can be applied to the radar signal angle measurement system based on spatial spectrum estimation described above.

[0125] See also Fig. 9 , an embodiment of the present application provides a radar signal angle measurement method based on spatial spectrum estimation, including: step 110 and step 120.

[0126] Step 110 sends the steering vector corresponding to the angle range to be searched to the FPGA through the microprocessor;

[0127] In step 120, the spatial spectrum estimation function is determined by the FPGA according to the eigenvalues ​​and steering vectors of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured, and the angle at which the spatial spectrum estimation function reaches a maximum value is used as the angle of the radar signal to be measured by the FPGA.

[0128] The radar signal angle measurement method based on spatial spectrum estimation provided in the embodiment of the present application improves the response speed when calculating the spatial spectrum estimation function, thereby improving the real-time performance of measuring the angle of the radar signal to be measured based on the spatial spectrum estimation function, by centralizing the calculation of the spatial spectrum estimation function in the FPGA and sending a steering vector corresponding to the angle range to be searched required by the spatial spectrum estimation function for estimating the angle of the radar signal to be measured to the FPGA through a microprocessor.

[0129] It should be understood that expressions such as "including" and "may include" that may be used in the present application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In the present application, terms such as "including" and / or "having" may be interpreted as indicating specific characteristics, numbers, operations, constituent elements, components, or combinations thereof, but may not be interpreted as excluding the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0130] In addition, in the present application, the expression "and / or" includes any and all combinations of the associated listed words. For example, the expression "A and / or B" may include A, may include B, or may include both A and B.

[0131] In the description of the embodiments of the present application, it should be noted that, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a detachable connection or a non-detachable connection; it can be a direct connection or an indirect connection through an intermediate medium. Among them, "fixed connection" means that the relative position relationship after connection remains unchanged. "Rotational connection" means that the two are connected to each other and can rotate relative to each other after connection. "Sliding connection" means that the two are connected to each other and can slide relative to each other after connection. The directional terms mentioned in the embodiments of the present application, such as "top", "bottom", "inside", "outside", "left", "right", etc., are only reference directions of the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation and understanding of the embodiments of the present application, 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 a limitation on the embodiments of the present application.

[0132] In addition, in the embodiments of the present application, the mathematical concepts mentioned are symmetry, equality, parallelism, verticality, etc. These limitations are all for the current state of the art, rather than being absolutely strict definitions in a mathematical sense, and allow a small amount of deviation, approximation to symmetry, approximation to equality, approximation to parallelism, approximation to verticality, etc. are all possible. For example, A and B are parallel, which means that A and B are parallel or approximately parallel, and the angle between A and B can be between 0 and 10 degrees. A and B are perpendicular, which means that A and B are perpendicular or approximately perpendicular, and the angle between A and B can be between 80 and 100 degrees.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A radar signal angle measurement system based on spatial spectrum estimation, characterized in that: include: A microprocessor and a field programmable gate array FPGA connected to the microprocessor; The microprocessor is used to send the steering vector corresponding to the angle range to be searched to the FPGA; The FPGA is used to determine a spatial spectrum estimation function according to the eigenvalue of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured and the steering vector, and use the angle at which the spatial spectrum estimation function obtains a maximum value as the angle of the radar signal to be measured.

2. The radar signal angle measurement system based on spatial spectrum estimation according to claim 1, characterized in that: The FPGA comprises: The module for solving the autocorrelation matrix is ​​used to solve the autocorrelation matrix.

3. The radar signal angle measurement system based on spatial spectrum estimation according to claim 2, characterized in that: The FPGA further comprises: An autocorrelation matrix decomposition module is connected to the autocorrelation matrix solving module, and is used to perform Jacobi rotation on the autocorrelation matrix, adjust the non-diagonal elements in the autocorrelation matrix, and determine the diagonal matrix corresponding to the autocorrelation matrix; According to the diagonal matrix, eigenvalues ​​of the autocorrelation matrix are determined.

4. The passive radar angle measurement system based on spatial spectrum estimation according to claim 1, characterized in that: The FPGA further comprises: A data acquisition module, used for acquiring the radar signal to be tested; A data processing module, connected to the data acquisition module, for detecting the radar signal to be measured and caching certain snapshot data points; A Hilbert filter is connected to the data processing module and is used to perform a Hilbert filter transformation on the data points to convert the data points into the complex signals.

5. The radar signal angle measurement system based on spatial spectrum estimation according to claim 3, characterized in that: The autocorrelation matrix decomposition module is also used for: Determine whether the autocorrelation matrix is ​​the first Jacobi rotation, and if so, rotate the autocorrelation matrix with any non-diagonal element in the autocorrelation matrix as the center, and adjust any non-diagonal element in the autocorrelation matrix; Otherwise, repeatedly rotating the non-diagonal element with the largest amplitude in the autocorrelation matrix as the center, and adjusting the non-diagonal element with the largest amplitude until the maximum number of repetitions is reached; The diagonal matrix is ​​determined according to the autocorrelation matrix after the last execution.

6. The radar signal angle measurement system based on spatial spectrum estimation according to claim 3, characterized in that: The autocorrelation matrix decomposition module is also used for: The diagonal elements in the diagonal matrix are sorted in descending order, and the eigenvalues ​​are determined according to a first preset number of diagonal elements.

7. The radar signal angle measurement method based on spatial spectrum estimation according to claim 1, characterized in that: The FPGA further comprises: A spectrum function construction module is used to construct the spatial spectrum estimation function according to the eigenvector corresponding to the eigenvalue and the steering vector.

8. The passive radar angle measurement system based on spatial spectrum estimation according to claim 7, characterized in that: The spectrum function building module is also used for: The spatial spectrum estimation function is constructed based on the following formula: Wherein, P(θ) is the spatial spectrum estimation function, a(θ) is the steering vector, V u is the feature vector.

9. The passive radar angle measurement system based on spatial spectrum estimation according to claim 4, characterized in that: The data processing module comprises: A detection pulse module, connected to the data acquisition module, for detecting the radar signal to be measured; A first-in-first-out buffer is connected to the detection pulse module and is used to buffer certain snapshot data points.

10. A radar signal angle measurement method based on spatial spectrum estimation, characterized in that: The radar signal angle measurement system based on spatial spectrum estimation as claimed in any one of claims 1 to 9 is characterized by comprising: The steering vector corresponding to the angle range to be searched is sent to the FPGA via the microprocessor; The spatial spectrum estimation function is determined by FPGA according to the eigenvalue of the autocorrelation matrix of the complex signal corresponding to the radar signal to be measured and the steering vector, and the angle at which the spatial spectrum estimation function reaches a maximum value is used as the angle of the radar signal to be measured by FPGA.

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