Pulse compression method and apparatus based on nonlinear frequency modulation, device and medium

Abstract

The application relates to a pulse compression method and device based on nonlinear frequency modulation, equipment and medium. The method comprises the following steps: constructing a toplyitz matrix based on a discrete sequence of each candidate signal; the candidate signal is a nonlinear frequency modulation signal; constructing a target function of each candidate signal based on each toplyitz matrix and a preset expected output sequence; minimizing each target function to obtain a filter coefficient vector corresponding to each candidate signal; calculating a comprehensive score of each candidate signal based on each filter coefficient vector, and determining a transmission signal based on the comprehensive score of each candidate signal; performing compression processing on an echo signal of the transmission signal by using a receiving filter corresponding to the transmission signal; and the receiving filter is obtained based on a filter coefficient vector corresponding to the transmission signal. The method can solve the problem that sidelobe suppression, signal-to-noise ratio loss and distance resolution are difficult to be considered in the traditional scheme.

Description

Technical Field

[0001] This application relates to the field of radar signal processing technology, and in particular to a pulse compression method, apparatus, device and medium based on nonlinear frequency modulation. Background Technology

[0002] In the field of radar detection technology, pulse compression technology is commonly used in engineering to balance detection range and range resolution. This technology ensures transmission energy by transmitting a frequency-modulated pulse signal (such as a linear frequency modulated signal) with a large time-width-bandwidth product, and then uses a matched filter at the receiving end to process the echo signal and compress it into a narrow pulse, thereby achieving both high signal-to-noise ratio and fine range resolution.

[0003] In pulse compression, Linear Frequency Modulation (LFM) signals are the most widely used radar waveform due to their simplicity, low hardware requirements, and ease of constructing matched filters. However, traditional LFM signals, after processing with matched filters, exhibit high range sidelobes. In complex scenarios such as weather radar detection, this high sidelobe characteristic can cause strong clutter or heavy precipitation echoes to overwhelm nearby weak target signals, severely affecting the identification and detection of weak echoes. To address the high sidelobe problem, existing technologies typically employ methods such as windowing at the transmitter, frequency domain weighting, designing nonlinear frequency modulation (NLFM) waveforms, or designing mismatched filters. While these methods can reduce sidelobe levels to some extent, they often lead to main lobe broadening, thereby reducing range resolution and resulting in a loss of signal-to-noise ratio (SNR). Therefore, a processing method that achieves low sidelobes without excessively sacrificing SNR and range resolution is urgently needed. Summary of the Invention

[0004] Therefore, it is necessary to provide a pulse compression method, apparatus, device, and medium based on nonlinear frequency modulation to address the aforementioned technical problems.

[0005] A pulse compression method based on nonlinear frequency modulation, the method comprising: S1. Construct the Toplitz matrix based on the discrete sequences of each candidate signal; the candidate signal is a nonlinear frequency-modulated signal. S2. Based on each of the Topulitz matrices A and the preset desired output sequence d, construct the objective function for each of the candidate signals respectively; S3. Minimize each of the objective functions to obtain the filter coefficient vector h corresponding to each of the candidate signals; S4. Based on the filter coefficient vectors, calculate the comprehensive score of each candidate signal, and determine the transmitted signal based on the comprehensive score of each candidate signal; S5. The echo signal of the transmitted signal is compressed using the receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal. Wherein, the objective function is ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter.

[0006] In this application, a Toplitz matrix is ​​constructed based on the discrete sequences of each candidate signal, and an objective function is constructed for each candidate signal based on each Toplitz matrix and a preset desired output sequence. This allows us to leverage the Tikhonov regularization term introduced in the objective function. and weighted least squares error term This transforms the optimization objective of the objective function into sidelobe suppression and coefficient smoothing. Thus, when minimizing the objective function to obtain the filter coefficient vectors corresponding to each candidate signal, a filter coefficient vector that balances sidelobe suppression and coefficient smoothness can be obtained. This allows the receiving filter obtained based on the filter coefficient vector to approximate the smoothing characteristics of the matched filter. Consequently, by using the receiving filter corresponding to the transmitted signal to compress the echo signal of the transmitted signal, the energy of the echo signal can be preserved to the maximum extent while suppressing strong clutter sidelobe interference. This solves the technical problem in traditional schemes where it is difficult to balance sidelobe suppression with signal-to-noise ratio loss and range resolution.

[0007] In one embodiment, step S4 includes: The Toplitz matrix and the normalized filter coefficient vector corresponding to each candidate signal are convolved to obtain the pulse compressed output sequence of each candidate signal; the normalized filter coefficient vector is the result of normalizing the filter coefficient vector corresponding to the candidate signal. Based on the pulse compression output sequence of each candidate signal, the signal-to-noise ratio loss, peak sidelobe ratio, integral sidelobe ratio, and 3 dB main lobe width of each candidate signal are calculated respectively. From each of the candidate signals, determine the signal-to-noise ratio loss Loss. SNR The peak sidelobe ratio (PSLR), the integral sidelobe ratio (ISLR), and the three-decibel main lobe width (Width) are mentioned. 3dB The target signal that meets the preset conditions; the preset conditions are that the signal-to-noise ratio loss is less than a first threshold, the peak sidelobe ratio is greater than a second threshold, the integral sidelobe ratio is less than a third threshold, and the main lobe width is less than a fourth threshold. pass Calculate the overall score for each target signal; The weight of the integral sidelobe ratio, The weight of the main lobe width is given by 3 dB. The weight of the main lobe width in 3 dB; The target signal with the highest comprehensive score is determined as the transmitted signal.

[0008] In this application, the signal-to-noise ratio loss (Loss) is determined from each candidate signal. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB The target signal meets the preset conditions, which are: signal-to-noise ratio loss less than a first threshold, peak sidelobe ratio greater than a second threshold, integral sidelobe ratio less than a third threshold, and main lobe width less than a third 3 dB. Calculate the overall score of each target signal and determine the target signal with the highest overall score as the transmitted signal. This avoids the use of a receiver filter with theoretically low sidelobes but difficult to implement in engineering when compressing the echo signal of the transmitted signal using the receiver filter corresponding to the transmitted signal. Thus, it is possible to use an easily implemented receiver filter to compress the echo signal of the transmitted signal.

[0009] In one embodiment, the method further includes: If there is no target signal that meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, one or more of the waveform parameters of the filter coefficient vector, regularization coefficient, and instantaneous frequency function are adjusted, and the transmitted signal is determined based on the adjusted parameters.

[0010] In this application, when there is no target signal that meets preset conditions, or when the highest comprehensive score among the comprehensive scores of the target signal is lower than a score threshold, one or more of the waveform parameters of the filter coefficient vector, regularization coefficient, and instantaneous frequency function are adjusted, and the transmitted signal is determined based on the adjusted parameters. This allows the signal-to-noise ratio loss (SNR) to be obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB Transmitted signals that meet preset conditions and whose comprehensive score is greater than or equal to the score threshold ensure that the energy of the echo signal is not excessively weakened after pulse compression, thus maintaining the radar's maximum detection range and sensitivity for weak targets. Furthermore, it ensures that the main lobe does not widen excessively, allowing the radar to still possess extremely high range resolution and clearly distinguish between two targets at close range.

[0011] In one embodiment, the process of determining the receiving filter corresponding to the transmitted signal includes: Determine the filter coefficient vector corresponding to the transmitted signal; pass The filter coefficient vector corresponding to the transmitted signal is normalized to obtain the normalized filter coefficient vector h corresponding to the transmitted signal. new ; This represents the average of the squares of each filter coefficient in the filter coefficient vector; Based on the normalized filter coefficient vector, a receiving filter is determined for compressing the echo signal of the transmitted signal.

[0012] In this application, the filter coefficient vector corresponding to the transmitted signal is determined by... The filter coefficient vector corresponding to the transmitted signal is normalized to obtain the normalized filter coefficient vector h corresponding to the transmitted signal. new Based on the normalized filter coefficient vector, the receiving filter used to compress the echo signal of the transmitted signal is determined. This ensures that the gain characteristics of the receiving filter are stable regardless of the absolute amplitude of the echo signal, and also facilitates the fixed-point implementation of subsequent field-programmable gate arrays or digital signal processors.

[0013] In one embodiment, the process of acquiring each candidate signal in step S1 includes: Determine multiple instantaneous frequency functions; Based on each of the instantaneous frequency functions f inst (t), through and Multiple candidate signals s(t) are obtained; t is time. This refers to the signal phase.

[0014] In this application, by determining multiple instantaneous frequency functions, it is possible to base the method on each instantaneous frequency function f. inst (t), through and Multiple candidate signals s(t) are obtained.

[0015] In one embodiment, the instantaneous frequency function includes a polynomial instantaneous frequency function, a hyperbolic tangent instantaneous frequency function, a sinusoidal instantaneous frequency function, and a stationary phase method instantaneous frequency function based on the target spectrum.

[0016] In one embodiment, the matched filter h corresponding to each candidate signal in the objective function match The formula for obtaining it is: ; Here, reverse() means sequence reversal, conj() means complex conjugation operation, and s is the discrete sequence of candidate signals.

[0017] In this application, based on Determine the matched filter h corresponding to each candidate signal match This allows us to construct an ideal filter that maximizes the output signal-to-noise ratio.

[0018] A pulse compression device based on nonlinear frequency modulation, the device comprising: A matrix construction module is used to construct Toplitz matrices based on the discrete sequences of each candidate signal; the candidate signals are nonlinear frequency-modulated signals. The function construction module is used to construct the objective function of each candidate signal based on each of the Topulitz matrices A and the preset expected output sequence d; The solution module is used to minimize each of the objective functions to obtain the filter coefficient vector h corresponding to each of the candidate signals; A signal determination module is used to calculate a comprehensive score for each candidate signal based on each of the filter coefficient vectors, and to determine the transmitted signal based on the comprehensive score of each candidate signal; A pulse compression module is used to compress the echo signal of the transmitted signal using a receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal; wherein, the objective function is... ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter.

[0019] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0020] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0021] The aforementioned pulse compression device, equipment, and medium based on nonlinear frequency modulation construct a Toplitz matrix based on the discrete sequences of each candidate signal, and constructs an objective function for each candidate signal based on each Toplitz matrix and a preset desired output sequence. This allows us to leverage the Tikhonov regularization term introduced in the objective function. and weighted least squares error term This transforms the optimization objective of the objective function into sidelobe suppression and coefficient smoothing. Thus, when minimizing the objective function to obtain the filter coefficient vectors corresponding to each candidate signal, a filter coefficient vector that balances sidelobe suppression and coefficient smoothness can be obtained. This allows the receiving filter obtained based on the filter coefficient vector to approximate the smoothing characteristics of the matched filter. Consequently, by using the receiving filter corresponding to the transmitted signal to compress the echo signal of the transmitted signal, the energy of the echo signal can be preserved to the maximum extent while suppressing strong clutter sidelobe interference. This solves the technical problem in traditional schemes where it is difficult to balance sidelobe suppression with signal-to-noise ratio loss and range resolution. Attached Figure Description

[0022] Figure 1 This is an application environment diagram of a pulse compression method based on nonlinear frequency modulation in one embodiment; Figure 2 This is a flowchart illustrating a pulse compression method based on nonlinear frequency modulation in one embodiment; Figure 3 This is a global graph of the compression results in one embodiment; Figure 4 This is a schematic diagram illustrating the relationship between the regularization parameter λ and sidelobe suppression and signal-to-noise ratio loss in one embodiment; Figure 5 This is a schematic diagram comparing the instantaneous frequency curves of different types of candidate signals in one embodiment; Figure 6 This is a schematic diagram of the peak sidelobe ratio of different types of candidate signals in one embodiment; Figure 7 This is a schematic diagram of the overall process of a pulse compression method based on nonlinear frequency modulation in another embodiment; Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0024] The pulse compression method based on nonlinear frequency modulation provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 interacts with server 104 via a wired / wireless channel. A data storage system can store the data that server 104 needs to process. The server constructs a Toplitz matrix based on the discrete sequences of each candidate signal; the candidate signal is a nonlinear frequency-modulated signal. Based on each Toplitz matrix A and a preset desired output sequence d, the server constructs an objective function for each candidate signal. The server minimizes each objective function to obtain the filter coefficient vector h corresponding to each candidate signal. Based on each filter coefficient vector, the server calculates the comprehensive score of each candidate signal and determines the transmitted signal based on the comprehensive score. The server uses the receiving filter corresponding to the transmitted signal to compress the echo signal of the transmitted signal. The receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal. The objective function is... ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, etc. Server 104 can be a single server, a server cluster consisting of multiple servers, or a cloud computing center consisting of multiple servers.

[0025] In one embodiment, such as Figure 2 As shown, a pulse compression method based on nonlinear frequency modulation is provided, which is applied to... Figure 1 Taking server 104 as an example, the following steps are included: S1. Construct the Toplitz matrix based on the discrete sequences of each candidate signal; the candidate signals are nonlinear frequency-modulated signals. The discrete sequence of the candidate signal is represented as follows: s N-1 This represents the Nth sampling point in the discrete sequence.

[0026] The Toeplitz matrix is ​​the convolution matrix. A Toeplitz matrix constructed from discrete sequences is represented as follows: The dimension of the Toplitz matrix is ​​N×(M-1), where N is the number of sampling points in the discrete sequence and M is the number of filter coefficients in the filter coefficient vector.

[0027] Nonlinear Frequency Modulation (NLFM) can reduce sidelobes. This is mainly because NLFM breaks the linear constant-rate change of instantaneous frequency and cleverly utilizes the difference in dwell time in different frequency bands to achieve equivalent frequency domain amplitude weighting (spectral shaping) within the transmitted signal. Thus, low sidelobes are directly obtained after pulse compression, avoiding the main lobe broadening and signal-to-noise ratio loss caused by traditional time-domain weighting.

[0028] S2. Based on each Topplitz matrix A and the preset desired output sequence d, construct the objective function for each candidate signal respectively; The desired output sequence is the shape of the compressed echo signal. For example, the desired compressed echo signal should retain the shape of the matched filter main lobe near the main lobe center and approach zero in the sidelobe regions outside the main lobe. An objective function is constructed based on the Topulitz matrix A and the preset desired output sequence d, which can suppress the sidelobes while protecting the main lobe and improving range resolution. The desired output sequence can be preset. It can retain the matched filter main lobe or use a preset main lobe template.

[0029] S3. Minimize each objective function to obtain the filter coefficient vector h corresponding to each candidate signal; The filter coefficient vector is the parameter obtained by minimizing the objective function. Each candidate signal's objective function can be minimized to obtain a filter coefficient vector; that is, each candidate signal corresponds to a filter coefficient vector.

[0030] S4. Based on the coefficient vectors of each filter, calculate the comprehensive score of each candidate signal, and determine the transmitted signal based on the comprehensive score of each candidate signal. S5. Compress the echo signal of the transmitted signal using the receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal. Wherein, the objective function is ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter, also known as the filter coefficient vector of the matched filter. This represents the square of the vector's L2 norm, which is the sum of the squares of its elements. By using a matched filter as a reference constraint in the objective function, severe oscillations in the mismatched filter coefficients can be suppressed, improving tolerance to noise, quantization errors, and minor waveform deviations.

[0031] The specific steps to minimize the objective function and obtain the filter coefficient vector are as follows: S31, the objective function J(h) is expanded as follows: Among them, the first four items ( ) comes from the weighted least squares error, the last term ( This comes from regularization constraints; S32, let the objective function right The partial derivatives are zero, therefore we get ; S33, will Contains Move the item to the left to get ;in, This represents the identity matrix, whose dimensions are the same as the length of the filter coefficient vector. S33, according to The closed-form solution is obtained as This yields the specific result of the filter coefficient vector.

[0032] The comprehensive score can be obtained based on the signal-to-noise ratio (SNR) loss, peak sidelobe ratio (PSNR), integral sidelobe ratio (INR), and 3dB main lobe width of each candidate signal. The SNR loss, PSNR, INR, and INR width are derived from the filter coefficient vector. The transmitted signal can be the candidate signal with the highest comprehensive score, or it can be the target signal with the highest comprehensive score that meets preset conditions for SNR loss, PSNR, INR, and INR width.

[0033] The weight matrix W controls the relative importance of errors in different distance cells. A distance cell belonging to a sidelobe region is assigned a larger weight, while a distance cell belonging to the main lobe protection region is assigned a smaller weight. This allows the optimization process to prioritize minimizing sidelobe region errors. The weight matrix W can be configured using piecewise constant weights, distance-dependent weights, multi-objective region weights, or adaptive weights, depending on the task.

[0034] The regularization coefficient is a real number greater than or equal to 0. When the regularization coefficient is 0, the minimization objective function degenerates into a weighted least squares problem without regularization constraints. A regularization coefficient greater than 0 can limit the degree to which the receiving filter deviates from the matched filter. The larger the regularization coefficient, the closer the solved filter coefficient vector is to the matched filter coefficient vector, and the lower the signal-to-noise ratio loss, but the sidelobe suppression capability decreases; the smaller the regularization coefficient, the more the filter corresponding to the solved filter coefficient vector tends to suppress sidelobes, but it may bring greater mismatch loss and numerical sensitivity.

[0035] The objective function can also be called the error cost function. In order to make the pulse compression output sequence as close as possible to the desired output sequence, the objective function can be minimized by weighted least squares objective.

[0036] The meaning of the Tikhonov regularization term is to penalize the new filter (the receiving filter corresponding to the filter coefficient vector) for excessive deviation from the matched filter h. match Because the receiving filter may suppress sidelobes to extremely low levels during compression processing, resulting in severe coefficient oscillations, which leads to noise amplification, increased signal-to-noise ratio loss, and greater sensitivity to Doppler shift, quantization errors, and hardware implementation errors, a Tikhonov regularization term is introduced into the objective function. This is to ensure that the receiving filter corresponding to the final filter coefficient vector can avoid suppressing the sidelobes to extremely low levels.

[0037] The receiving filter can be obtained directly from the filter coefficient vector, or it can be obtained from the normalized result of the filter coefficient vector.

[0038] Based on the compressed results, target distances can be accurately measured and dense or overlapping targets can be distinguished.

[0039] Furthermore, the objective function can also be .

[0040] Furthermore, the regularization term in the objective function ( It can also be any one of the following: filter smoothing constraint, frequency domain amplitude constraint, dynamic range constraint, Doppler robustness constraint, or hardware quantization error constraint.

[0041] In one specific embodiment, the echo signal of the transmitted signal is compressed using the receiving filter corresponding to the transmitted signal. The resulting global diagram is shown below. Figure 3 As shown.

[0042] In the aforementioned pulse compression method based on nonlinear frequency modulation, a Topulitz matrix is ​​constructed based on the discrete sequences of each candidate signal. Based on each Topulitz matrix and a preset desired output sequence, an objective function is constructed for each candidate signal. This allows us to leverage the Tikhonov regularization term introduced in the objective function. and weighted least squares error term This transforms the optimization objective of the objective function into sidelobe suppression and coefficient smoothing. Thus, when minimizing the objective function to obtain the filter coefficient vectors corresponding to each candidate signal, a filter coefficient vector that balances sidelobe suppression and coefficient smoothness can be obtained. This allows the receiving filter obtained based on the filter coefficient vector to approximate the smoothing characteristics of the matched filter. Consequently, by using the receiving filter corresponding to the transmitted signal to compress the echo signal of the transmitted signal, the energy of the echo signal can be preserved to the maximum extent while suppressing strong clutter sidelobe interference. This solves the technical problem in traditional schemes where it is difficult to balance sidelobe suppression with signal-to-noise ratio loss and range resolution.

[0043] In one embodiment, step S4 includes: The Topplitz matrix and the normalized filter coefficient vector corresponding to each candidate signal are convolved to obtain the pulse compressed output sequence of each candidate signal; the normalized filter coefficient vector is the result of normalizing the filter coefficient vector corresponding to the candidate signal. Based on the pulse compression output sequence of each candidate signal, the signal-to-noise ratio loss, peak sidelobe ratio, integral sidelobe ratio, and 3 dB main lobe width of each candidate signal are calculated respectively. Determine the signal-to-noise ratio loss from each candidate signal. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB The target signal that meets the preset conditions; the preset conditions are that the signal-to-noise ratio loss is less than the first threshold, the peak sidelobe ratio is greater than the second threshold, the integral sidelobe ratio is less than the third threshold, and the main lobe width of 3 dB is less than the fourth threshold. pass Calculate the overall score for each target signal; The weights for the integral sidelobe ratio, The weight of the main lobe width is 3 volts. The weight of the main lobe width is 3 dB; The target signal with the highest overall score is selected as the transmitted signal.

[0044] Here, the normalized filter coefficient vector corresponding to the candidate signal is the result of normalizing the filter coefficient vector corresponding to the candidate signal. The Toplitz matrix corresponding to the candidate signal is a matrix obtained through the discrete sequence of the candidate signal. For example, convolving the normalized filter coefficient vector and the Toplitz matrix corresponding to candidate signal A yields the pulse compressed output sequence of candidate signal A; convolving the normalized filter coefficient vector and the Toplitz matrix corresponding to candidate signal B yields the pulse compressed output sequence of candidate signal B.

[0045] The formula for calculating the peak-to-side-lobe ratio is: ; The maximum value among all sampling points in the pulse compression output sequence, excluding the main lobe region, represents the most severe false target or interference; The peak value of the main lobe in the pulse compression output sequence is the maximum amplitude in the pulse compression output sequence.

[0046] The formula for calculating the integral sidelobe ratio is: ; Let be the amplitude of the i-th sidelobe sampling point in the pulse compression output sequence, and P be the number of sidelobe sampling points in the pulse compression output sequence.

[0047] The formula for calculating the main lobe width in 3 dB is Width.3dB =t2-t1, where ; t2 and t1 are the periods when the amplitude of the main lobe decreases to its peak value. (That is, the power drops by half, corresponding to -3dB) at two time points, y(t1) is the signal amplitude corresponding to time t1 in the pulse compression output sequence, and y(t2) is the signal amplitude corresponding to time t2 in the pulse compression output sequence.

[0048] The formula for calculating signal-to-noise ratio loss is: , The output amplitude of the main lobe after filtering the echo signal of the candidate signal using a matched filter. The energy of the filter coefficients of the matched filter. The main lobe output amplitude after filtering the echo signal of the candidate signal using the receiving filter corresponding to the candidate signal. The energy of the normalized filter coefficient vector corresponding to the candidate signal; the normalized filter coefficient vector corresponding to the candidate signal is obtained by normalizing the filter coefficient vector corresponding to the candidate signal; the receiving filter corresponding to the candidate signal is obtained from the normalized filter coefficient vector corresponding to the candidate signal.

[0049] , and It can be configured based on radar system, meteorological mission, or hardware capabilities. , and The higher the values ​​of these three parameters, the more importance is placed on this indicator. In practical use, the weight... , and It can be preset. For example, in scenarios where signal-to-noise ratio degradation is more critical, the weights... Set it higher, weight , and The sum of is 1 when the weights are equal. If the settings are too high, the other two parameters should be relatively reduced. For example, in quantitative precipitation estimation scenarios, the weights of the integral sidelobe ratio and the 3-decibel main lobe width can be increased; in long-distance weak echo detection scenarios, the weight of the signal-to-noise ratio loss can be increased.

[0050] The first, second, third, and fourth thresholds can all be preset. The fourth threshold is used to limit the width of the main lobe or the main lobe protection region after pulse compression. The second threshold is used to constrain the sidelobe level.

[0051] The pulse compression output sequence can also be understood as the output response after the echo signal is compressed. The pulse compression output sequence is also a convolution matrix model of pulse compression.

[0052] In this embodiment, the signal-to-noise ratio loss Loss is determined from each candidate signal. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB The target signal meets the preset conditions, which are: signal-to-noise ratio loss less than a first threshold, peak sidelobe ratio greater than a second threshold, integral sidelobe ratio less than a third threshold, and main lobe width less than a third 3 dB. Calculate the overall score of each target signal and determine the target signal with the highest overall score as the transmitted signal. This avoids the use of a receiver filter with theoretically low sidelobes but difficult to implement in engineering when compressing the echo signal of the transmitted signal using the receiver filter corresponding to the transmitted signal. Thus, it is possible to use an easily implemented receiver filter to compress the echo signal of the transmitted signal.

[0053] In one embodiment, the method further includes: If there is no target signal that meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, adjust one or more of the following: the length of the filter coefficient vector, the regularization coefficient, and the waveform parameters of the instantaneous frequency function, and determine the transmitted signal based on the adjusted parameters.

[0054] The waveform parameters of the instantaneous frequency function include, but are not limited to, bandwidth, coefficients p and a used to adjust the shape of the signal's time-frequency characteristics, and disturbance intensity parameters. η The length of the filter coefficient vector, the regularization coefficient, and the waveform parameters of the instantaneous frequency function can be adjusted using one or more of the following algorithms: traversal search, grid search, random search, Bayesian optimization, genetic algorithm, or other intelligent optimization algorithms.

[0055] If no target signal meets the preset conditions in the current round, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, adjust one or more of the following parameters: the length of the filter coefficient vector, the regularization coefficient, and the waveform parameters of the instantaneous frequency function. Then, perform the signal determination step based on the adjusted parameters. If, after performing the signal determination step based on the adjusted parameters, no target signal still meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, continue to repeat the process of "adjusting one or more of the length of the filter coefficient vector, the regularization coefficient, and the waveform parameters of the instantaneous frequency function, and performing the signal determination step based on the adjusted parameters" until the signal-to-noise ratio loss (Loss) is obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dBTransmitted signals that meet preset conditions and whose overall score is greater than or equal to a score threshold. The types of parameters adjusted in each round can be different. For example, the length of the filter coefficient vector might be adjusted in one round, while the regularization coefficient might be adjusted in the next.

[0056] Furthermore, if there is no target signal that meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the score threshold, the length of the filter coefficient vector is adjusted, and steps S1, S2, S3, S4, and S5 are executed based on the length-updated filter coefficient vector until the signal-to-noise ratio loss Loss is obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB Transmitted signals that meet preset conditions and whose comprehensive score is greater than or equal to the score threshold.

[0057] Furthermore, when there is no target signal that meets the preset conditions, or when the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, the regularization coefficient in the objective function is adjusted to obtain the adjusted objective function. S3, S4, and S5 are then executed based on the adjusted objective function until the signal-to-noise ratio loss Loss is obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB Transmitted signals that meet preset conditions and whose comprehensive score is greater than or equal to the score threshold.

[0058] Furthermore, when there is no target signal that meets the preset conditions, or when the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, the waveform parameters of the instantaneous frequency function are adjusted to obtain the adjusted instantaneous frequency function. The candidate signal corresponding to the adjusted instantaneous frequency function is then determined. Based on the candidate signal corresponding to the adjusted instantaneous frequency function, steps S1, S2, S3, S4, and S5 are executed until the signal-to-noise ratio loss (Loss) is obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB Transmitted signals that meet preset conditions and whose comprehensive score is greater than or equal to the score threshold.

[0059] In one specific embodiment, a schematic diagram illustrating the relationship between the regularization parameter λ and sidelobe suppression and signal-to-noise ratio loss is shown below. Figure 4 As shown, the regularization parameter λ is the regularization coefficient.

[0060] In this embodiment, when there is no target signal that meets the preset conditions, or when the highest comprehensive score among the comprehensive scores of the target signal is lower than the score threshold, one or more of the waveform parameters of the filter coefficient vector, regularization coefficient, and instantaneous frequency function are adjusted, and the transmitted signal is determined based on the adjusted parameters. This allows the signal-to-noise ratio loss (LOS) to be obtained. SNR Peak sidelobe ratio (PSLR), integral sidelobe ratio (ISLR), and main lobe width (3 dB) 3dB Transmitted signals that meet preset conditions and whose comprehensive score is greater than or equal to the score threshold ensure that the energy of the echo signal is not excessively weakened after pulse compression, thus maintaining the radar's maximum detection range and sensitivity for weak targets. Furthermore, it ensures that the main lobe does not widen excessively, allowing the radar to still possess extremely high range resolution and clearly distinguish between two targets at close range.

[0061] In one embodiment, if there is no target signal that meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, the final transmitted signal and the corresponding receiving filter can be determined by constraining at least one of the following: filter length, filter coefficient dynamic range, main lobe protection region, side lobe region weight, Doppler robustness, or hardware quantization error, so as to improve engineering feasibility and robustness.

[0062] In one embodiment, the process of determining the receiving filter corresponding to the transmitted signal includes: Determine the filter coefficient vector corresponding to the transmitted signal; pass The filter coefficient vector corresponding to the transmitted signal is normalized to obtain the normalized filter coefficient vector h corresponding to the transmitted signal. new ; This represents the average of the squares of each filter coefficient in the filter coefficient vector; Based on the normalized filter coefficient vector, the receiving filter used to compress the echo signal of the transmitted signal is determined.

[0063] in, This represents the average of the squares of the filter coefficients in the filter coefficient vector. For example, if the filter coefficients in the filter coefficient vector are 2, 3, 5, and 4, then... =14.5.

[0064] The normalized filter coefficient vector is configured for the receiver, and the configured normalized filter coefficient vector is the receiving filter.

[0065] In this embodiment, the filter coefficient vector corresponding to the transmitted signal is determined, and then... The filter coefficient vector corresponding to the transmitted signal is normalized to obtain the normalized filter coefficient vector h corresponding to the transmitted signal. new Based on the normalized filter coefficient vector, the receiving filter used to compress the echo signal of the transmitted signal is determined. This ensures that the gain characteristics of the receiving filter are stable regardless of the absolute amplitude of the echo signal, and also facilitates the fixed-point implementation of subsequent field-programmable gate arrays or digital signal processors.

[0066] In one embodiment, the process of acquiring each candidate signal in step S1 includes: Determine multiple instantaneous frequency functions; Based on each instantaneous frequency function f inst (t), through and Multiple candidate signals s(t) are obtained; t is time. This refers to the signal phase.

[0067] Instantaneous frequency can be understood as the frequency at which the radar is transmitting at a specific moment. inst (t) exhibits a nonlinear change over time, thus enabling the generation of different types of nonlinear frequency modulation signals.

[0068] Furthermore, the instantaneous frequency function includes polynomial-type instantaneous frequency functions, hyperbolic tangent-type instantaneous frequency functions, sinusoidal-type instantaneous frequency functions, and instantaneous frequency functions based on the target spectrum and the standing-phase method. A comparative diagram of the instantaneous frequency curves of different types of candidate signals is shown below. Figure 5 As shown. The peak sidelobes of different types of candidate signals, for example... Figure 6 As shown.

[0069] The instantaneous frequency function of polynomial form is expressed as ;f poly (t) represents the polynomial instantaneous frequency function; sign(x) represents the sign function, where x represents the normalized time variable, ranging from -1 to 1; B represents the bandwidth; p is a coefficient used to adjust the shape of the signal's time-frequency characteristics. p is a real number greater than 0 and is also a waveform parameter of the instantaneous frequency function. Different values ​​of p result in different frequency dwell distributions within the pulse, thus affecting the sidelobe level and main lobe shape after pulse compression. By setting different coefficients p, multiple polynomial instantaneous frequency functions can be obtained, allowing for the analysis of each polynomial instantaneous frequency function. and Multiple corresponding candidate signals were obtained.

[0070] The instantaneous frequency function of the hyperbolic tangent form is expressed as: x = 2t / T, where t is time, ranging from -T / 2 to T / 2, T is the pulse width, and x represents the normalized time variable, ranging from -1 to 1; f tanh (t) represents the instantaneous frequency function of the hyperbolic tangent, tanh() represents the hyperbolic tangent function, and 'a' is a coefficient used to adjust the shape of the signal's time-frequency characteristics. 'a' is a real number greater than 0 and controls the steepness of the hyperbolic tangent curve. A larger 'a' results in a faster change in the central region and a smoother change in the peripheral region; a smaller 'a' results in a more linear overall change. 'a' is also a waveform parameter of the instantaneous frequency function. By setting different coefficients 'a', multiple instantaneous frequency functions of the hyperbolic tangent can be obtained. Therefore, based on each instantaneous frequency function of the hyperbolic tangent, [the signal can be analyzed using...]. and Multiple corresponding candidate signals were obtained.

[0071] The sinusoidal instantaneous frequency function is expressed as follows: f sin (t) is a sinusoidal instantaneous frequency function, and sin() is a sine function. η For disturbance intensity parameters, η These are also waveform parameters of the instantaneous frequency function. By setting different disturbance intensity parameters, multiple sinusoidal instantaneous frequency functions can be obtained, and thus, based on each sinusoidal instantaneous frequency function, [the following can be achieved] through [the following]. and Multiple corresponding candidate signals were obtained.

[0072] Waveform parameters p, a, η When the frequency changes, the distribution of its dwell time at different positions within the pulse will change accordingly, thus affecting the pulse compression sidelobe level. Bandwidth B, pulse width T, and sampling rate can all be preset. Bandwidth B, pulse width T, and sampling rate can also be collectively referred to as radar system parameters. Bandwidth B is used to determine the frequency range covered by the transmitted signal, pulse width T is used to determine the duration of a single transmitted pulse, and sampling rate is used to determine the digital sampling rate.

[0073] In this embodiment, by determining multiple instantaneous frequency functions, it is possible to base the analysis on each instantaneous frequency function f. inst (t), through and Multiple candidate signals s(t) are obtained.

[0074] In one embodiment, the matched filter h corresponding to each candidate signal in the objective function match The formula for obtaining it is: ; Here, reverse() means sequence reversal, conj() means complex conjugation operation, and s is the discrete sequence of candidate signals.

[0075] Among them, the matched filter is the reference filter with the highest output signal-to-noise ratio in traditional theory.

[0076] During signal transmission, continuous signals are eventually sampled into discrete sequences. Therefore, the matched filter corresponding to each candidate signal is determined based on the discrete sequence of the candidate signals.

[0077] `reverse()` can be used to reverse the time axis of a signal. `conj()` can be used to invert the imaginary part of a complex signal.

[0078] This application does not directly use the matched filter as the final receiving filter, but rather uses it as a reference constraint object. This ensures that while pursuing advanced indicators such as low sidelobes, the most basic detection capability of the radar system is not lost, thus achieving the best engineering balance.

[0079] In this embodiment, based on Determine the matched filter h corresponding to each candidate signal match This allows us to construct an ideal filter that maximizes the output signal-to-noise ratio.

[0080] This application can be used not only for weather radar, but also for other pulse radar systems that require low sidelobe pulse compression, such as cloud radar, wind radar, surveillance radar and target detection radar.

[0081] This application also provides an application scenario in which the above-described pulse compression method based on nonlinear frequency modulation is applied. Specifically, the application of the pulse compression method based on nonlinear frequency modulation in this scenario is as follows: like Figure 7As shown, the server presets various parameters, including bandwidth B, pulse width T, sampling rate, first threshold, second threshold, third threshold, and fourth threshold. The server determines multiple instantaneous frequency functions and obtains multiple candidate signals based on these functions; the instantaneous frequency functions include polynomial, hyperbolic tangent, sinusoidal, and stationary phase type based on the target spectrum. The server determines the discrete sequence of each candidate signal and obtains the matched filter for each candidate signal by performing sequence flipping and complex conjugation operations on the discrete sequence. The server constructs the objective function for each candidate signal and minimizes the objective function to obtain the filter coefficient vector corresponding to each candidate signal. The server obtains the pulse compression output sequence using the filter coefficient vector and the Topolitz matrix constructed from the discrete sequence. Based on the pulse compression output sequence of each candidate signal, the server calculates the signal-to-noise ratio loss, peak sidelobe ratio, integral sidelobe ratio, and 3 dB main lobe width for each candidate signal. If the server determines that there is a target signal that meets the preset conditions and has the highest comprehensive score, and that the comprehensive score of the target signal is greater than or equal to the score threshold, then the target signal that meets the preset conditions and has the highest comprehensive score will be determined as the transmission signal. If it does not exist, the server will return to "determine multiple instantaneous frequency functions" to obtain the transmission signal that meets the preset conditions and has a comprehensive score greater than or equal to the score threshold.

[0082] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0083] Based on the same inventive concept, this application also provides a nonlinear frequency modulation-based pulse compression device for implementing the aforementioned nonlinear frequency modulation-based pulse compression method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the nonlinear frequency modulation-based pulse compression device provided below can be found in the limitations of the nonlinear frequency modulation-based pulse compression method described above, and will not be repeated here.

[0084] In one embodiment, a pulse compression device based on nonlinear frequency modulation is provided, comprising: A matrix construction module is used to construct Toplitz matrices based on the discrete sequences of each candidate signal; the candidate signals are nonlinear frequency-modulated signals. The function construction module is used to construct the objective function of each candidate signal based on each of the Topulitz matrices A and the preset expected output sequence d; The solution module is used to minimize each of the objective functions to obtain the filter coefficient vector h corresponding to each of the candidate signals; A signal determination module is used to calculate a comprehensive score for each candidate signal based on each of the filter coefficient vectors, and to determine the transmitted signal based on the comprehensive score of each candidate signal; A pulse compression module is used to compress the echo signal of the transmitted signal using a receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal; wherein, the objective function is... ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter.

[0085] The modules in the aforementioned pulse compression device based on nonlinear frequency modulation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0086] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores various types of data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a pulse compression method based on nonlinear frequency modulation.

[0087] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0089] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0090] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A pulse compression method based on nonlinear frequency modulation, characterized in that, The method includes: S1. Construct the Toplitz matrix based on the discrete sequences of each candidate signal; the candidate signal is a nonlinear frequency-modulated signal. S2. Based on each of the Topulitz matrices A and the preset desired output sequence d, construct the objective function for each of the candidate signals respectively; S3. Minimize each of the objective functions to obtain the filter coefficient vector h corresponding to each of the candidate signals; S4. Based on the filter coefficient vectors, calculate the comprehensive score of each candidate signal, and determine the transmitted signal based on the comprehensive score of each candidate signal; S5. The echo signal of the transmitted signal is compressed using the receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal. Wherein, the objective function is ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter; Step S4 includes: The Toplitz matrix and the normalized filter coefficient vector corresponding to each candidate signal are convolved to obtain the pulse compressed output sequence of each candidate signal; the normalized filter coefficient vector is the result of normalizing the filter coefficient vector corresponding to the candidate signal. Based on the pulse compression output sequence of each candidate signal, the signal-to-noise ratio loss, peak sidelobe ratio, integral sidelobe ratio, and 3 dB main lobe width of each candidate signal are calculated respectively. From each of the candidate signals, determine the signal-to-noise ratio loss Loss. SNR The peak sidelobe ratio (PSLR), the integral sidelobe ratio (ISLR), and the three-decibel main lobe width (Width) are mentioned. 3dB The target signal that meets the preset conditions; the preset conditions are that the signal-to-noise ratio loss is less than a first threshold, the peak sidelobe ratio is greater than a second threshold, the integral sidelobe ratio is less than a third threshold, and the main lobe width is less than a fourth threshold. pass Calculate the overall score for each target signal; The weight of the integral sidelobe ratio, The weight of the main lobe width is given by 3 dB. The weight of the main lobe width in 3 dB; The target signal with the highest comprehensive score is determined as the transmitted signal.

2. The method according to claim 1, characterized in that, The method further includes: If there is no target signal that meets the preset conditions, or if the highest comprehensive score among the comprehensive scores of the target signal is lower than the scoring threshold, one or more of the waveform parameters of the filter coefficient vector, regularization coefficient, and instantaneous frequency function are adjusted, and the transmitted signal is determined based on the adjusted parameters.

3. The method according to claim 1, characterized in that, The process of determining the receiving filter corresponding to the transmitted signal includes: Determine the filter coefficient vector corresponding to the transmitted signal; pass The filter coefficient vector corresponding to the transmitted signal is normalized to obtain the normalized filter coefficient vector h corresponding to the transmitted signal. new ; This represents the average of the squares of each filter coefficient in the filter coefficient vector; Based on the normalized filter coefficient vector, a receiving filter is determined for compressing the echo signal of the transmitted signal.

4. The method according to claim 1, characterized in that, The acquisition process of each candidate signal in step S1 includes: Determine multiple instantaneous frequency functions; Based on each of the instantaneous frequency functions f inst (t), through and Multiple candidate signals s(t) are obtained; t is time. This refers to the signal phase.

5. The method according to claim 4, characterized in that, The instantaneous frequency function includes polynomial instantaneous frequency functions, hyperbolic tangent instantaneous frequency functions, sinusoidal instantaneous frequency functions, and instantaneous frequency functions based on the target spectrum and the stationary phase method.

6. The method according to claim 1, characterized in that, The matched filter h corresponding to each candidate signal in the objective function match The formula for obtaining it is: ; Here, reverse() means sequence reversal, conj() means complex conjugation operation, and s is the discrete sequence of candidate signals.

7. A pulse compression device based on nonlinear frequency modulation, used to perform the method according to any one of claims 1-6, characterized in that, The device includes: A matrix construction module is used to construct Toplitz matrices based on the discrete sequences of each candidate signal; the candidate signals are nonlinear frequency-modulated signals. The function construction module is used to construct the objective function of each candidate signal based on each of the Topulitz matrices A and the preset expected output sequence d; The solution module is used to minimize each of the objective functions to obtain the filter coefficient vector h corresponding to each of the candidate signals; A signal determination module is used to calculate a comprehensive score for each candidate signal based on each of the filter coefficient vectors, and to determine the transmitted signal based on the comprehensive score of each candidate signal; A pulse compression module is used to compress the echo signal of the transmitted signal using a receiving filter corresponding to the transmitted signal; the receiving filter is obtained based on the filter coefficient vector corresponding to the transmitted signal; wherein, the objective function is... ; H is the regularization coefficient, H is the conjugate transpose, W is the weight matrix, and h is the weight matrix. match These are the filter coefficients of the matched filter.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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