Radar link periodic error correction method, system, equipment and medium

Abstract

The invention relates to the technical field of radar error correction, in particular to a radar link periodic error correction method, system and device and a medium, and the method comprises the steps: obtaining reference difference frequency information, carrying out the phase unwrapping of the reference difference frequency information, and obtaining phase error information; performing initial estimation on the phase error information according to a pre-constructed frequency estimation model and a pre-constructed frequency counting model to obtain first initial parameter information; constructing phase matching function information according to the first initial parameter information, and analyzing the phase matching function information and the phase error information to obtain second initial parameter information; performing first optimization processing according to the first initial parameter information and the second initial parameter information to obtain coarse optimization result information, and performing second optimization processing on the coarse optimization result information to obtain updated parameter information; the difference frequency information is corrected according to the updated parameter information, correction result information is obtained, periodic error correction is carried out more accurately, and the application effect of a radar system in a complex environment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of radar error correction, and in particular to a radar link periodic error correction method, system, device and medium. Background Art

[0002] Frequency modulated continuous wave radar FMCW is widely used in close-range safety detection, autonomous driving, deformation monitoring, and vital signs monitoring due to its advantages such as simple structure, low cost, and low intercept probability. In order to achieve higher-precision target distance resolution, radar systems usually need to have ultra-large instantaneous bandwidth. Ultra-large bandwidth places very high requirements on the acquisition system, and analog demodulation technology has become an important means to solve this problem. However, this technology has extremely strict requirements on the linear frequency modulation of the transceiver signal. Especially when the signal bandwidth is large, the deviation in linearity will cause serious blurring of the target distance resolution, and even inaccurate imaging. The transceiver link of the radar system is prone to introduce certain nonlinear errors. For example, common signal generation methods such as digital frequency synthesis DDS have nonlinear characteristics themselves. The linearity of the signal is further reduced by the influence of components such as frequency multipliers, mixers, and filters. The current nonlinear correction methods are divided into hardware and software. The hardware method minimizes FM nonlinearity by adjusting the signal generation circuit and can output high-linear FM signals in real time. However, it is affected by environmental factors such as temperature and has poor stability. In comparison, the software method has stronger versatility and adaptability and can perform nonlinear error correction on the sampled signal through digital signal processing technology.

[0003] The existing software correction method mainly estimates and corrects the nonlinearity of the transmission link, and uses a polynomial model to model the phase error. However, the periodic error is usually caused by factors such as DDS spurious and clock spurious, and the existing algorithm has not been able to effectively solve this problem. When fitting nonlinear parameters through wavelet transform and eigendecomposition technology, there are still problems such as high signal sampling rate requirements and performance degradation when the signal-to-noise ratio is low. In addition, the existing periodic error correction method is not accurate enough in frequency estimation, amplitude and phase parameter optimization, etc., which makes it impossible to meet the high-precision requirements in broadband radar applications, limiting the application effect of radar systems in complex environments. The above problems need to be solved. Summary of the invention

[0004] In order to more accurately perform periodic error correction and improve the application effect of the radar system in complex environments, the present application provides a radar link periodic error correction method, system, device and medium, which adopts the following technical solutions:

[0005] In a first aspect, the present application provides a radar link periodic error correction method, comprising:

[0006] Acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information;

[0007] Performing an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information;

[0008] Constructing phase matching function information according to the first initial parameter information, and analyzing the phase matching function information and the phase error information to obtain second initial parameter information;

[0009] Perform a first optimization process on the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain updated parameter information;

[0010] The difference frequency information is corrected according to the updated parameter information to obtain correction result information.

[0011] Preferably, the specific steps of performing initial estimation on the phase error information according to the pre-built frequency estimation model and frequency counting model to obtain the first initial parameter information are:

[0012] According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

[0013] Preferably, the specific steps of performing initial estimation on the phase error information according to the pre-built frequency estimation model and frequency counting model to obtain the first initial parameter information are:

[0014] According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

[0015] Preferably, the phase matching function information is:

[0016]

[0017] in, is the initial frequency value information, and K is the frequency number information.

[0018] Preferably, the specific steps of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are:

[0019] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0020] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0021] Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

[0022] Preferably, the specific step of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information further includes:

[0023] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0024] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0025] Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

[0026] Preferably, it also includes:

[0027] The first optimization process is a rough optimization performed by using the Adam algorithm;

[0028] The second optimization process is to perform fine optimization using the L-BFGS algorithm.

[0029] In a second aspect, the present application provides a radar link periodic error correction system, comprising:

[0030] An information acquisition module is used to acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information;

[0031] A first initial parameter analysis module, used to perform an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information;

[0032] A second initial parameter analysis module is used to construct phase matching function information according to the first initial parameter information, and analyze the phase matching function information and the phase error information to obtain second initial parameter information;

[0033] An update parameter analysis module is used to perform a first optimization process according to the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain update parameter information;

[0034] The correction module is used to correct the difference frequency information according to the updated parameter information to obtain correction result information.

[0035] Preferably, the first initial parameter analysis module performs an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model, and the specific steps of obtaining the first initial parameter information are:

[0036] According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

[0037] Preferably, the first initial parameter analysis module performs an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model, and the specific steps of obtaining the first initial parameter information are:

[0038] According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

[0039] Preferably, the phase matching function information of the second initial parameter analysis module is:

[0040]

[0041] in, is the initial frequency value information, and K is the frequency number information.

[0042] Preferably, the specific steps of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are:

[0043] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0044] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0045] Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

[0046] Preferably, the specific step of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information further includes:

[0047] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0048] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0049] Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

[0050] Preferably, it also includes:

[0051] The first optimization process of the update parameter analysis module is to perform rough optimization through the Adam algorithm;

[0052] The second optimization process of the update parameter analysis module is to perform fine optimization through the L-BFGS algorithm.

[0053] In a third aspect, the present application provides a radar link periodic error correction device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the radar link periodic error correction method as described above.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the radar link periodic error correction method as described above when running.

[0055] In summary, compared with the prior art, the technical solution provided by this application has at least the following beneficial effects:

[0056] The present application obtains reference difference frequency information containing a periodic phase error, performs phase unwrapping on the reference difference frequency information to obtain phase error information, uses a frequency estimation model and a frequency technology model to make a preliminary estimate of the periodic phase error, thereby obtaining first initial parameter information of the periodic phase error, adopts a phase matching method based on the first initial parameter information to obtain second initial parameter information of the periodic phase error, uses an optimization algorithm to process the first initial parameter information and the second initial parameter information, further optimizes to obtain updated parameter information, and finally corrects the difference frequency information based on the updated parameter information, thereby effectively eliminating the periodic phase error in the difference frequency information, more accurately performing periodic error correction, and improving the application effect of the radar system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a radar link periodic error correction method described in an embodiment of the present application.

[0058] Figure 2 It is a module diagram of a radar link periodic error correction system described in an embodiment of the present application.

[0059] Description of reference numerals:

[0060] 1. Information acquisition module; 2. First initial parameter analysis module; 3. Second initial parameter analysis module; 4. Update parameter analysis module; 5. Correction module. DETAILED DESCRIPTION

[0061] The following combination Figure 1-Figure 2 The present application is described in further detail. The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to be limiting.

[0062] Reference Figure 1 , a radar link periodic error correction method involved in this application specifically includes:

[0063] Acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information;

[0064] Performing an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information;

[0065] Constructing phase matching function information according to the first initial parameter information, and analyzing the phase matching function information and the phase error information to obtain second initial parameter information;

[0066] Perform a first optimization process on the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain updated parameter information;

[0067] The difference frequency information is corrected according to the updated parameter information to obtain correction result information.

[0068] Specifically, the periodic phase error estimation and correction of the present application is performed based on periodic phase error modeling and analysis. First, the difference frequency error model and its periodic phase distortion analysis are completed, and then the periodic phase error estimation and correction process is performed.

[0069] The periodic phase error modeling and analysis is as follows: first, the difference frequency signal error modeling is performed. The ideal FMCW linear frequency modulation continuous wave RF signal model is expressed as:

[0070]

[0071] Among them, f c is the carrier frequency, K r is the frequency modulation slope, which is defined as the ratio of bandwidth to modulation period, t is time, and the received signal is represented by the frequency modulation signal after a time delay τ of the transmitted signal, expressed as:

[0072]

[0073] in, is the signal delay, R is the target distance, and c is the speed of light.

[0074] After mixing the received signal and the transmitted signal, namely Dechirp, a difference frequency signal with nonlinear error can be obtained, which is:

[0075]

[0076] Where Kτt represents the target difference frequency, is the residual video phase (RVP). In near-field imaging, the time delay is very small, so the RVP term will be ignored in subsequent processing. This application further models the phase error of the difference frequency signal as the sum of a polynomial phase set and a sine function set:

[0077]

[0078] Where L is the polynomial error order, p is the polynomial coefficient. M is the number of periodic phase error sinusoidal signals, a k ,ω k and φ k are the amplitude, angular frequency and phase of the periodic phase error respectively, so the difference frequency signal model containing nonlinear phase error is:

[0079]

[0080] τ re f is the time delay of the reference target. After removing the polynomial phase error, the difference frequency signal is:

[0081]

[0082] This application performs periodic phase distortion analysis. The frequency modulation linearity of the LFM signal is usually defined as Performance Analysis of Frequency Sweep Nonlinearities in LFM Radars 1-4:

[0083]

[0084] Where, η represents the frequency modulation linearity of the LFM signal, B is the frequency modulation bandwidth of the LFM signal, and f e (t) Frequency shift frequency.

[0085] The periodic phase error is analyzed as follows: For the periodic frequency offset, the Fourier analysis point of view is adopted to decompose it into the sum of infinite simple harmonic waves. For the sake of simplicity of analysis, the frequency difference of a single frequency is introduced:

[0086] f e (t) = ηBcos(2πf m t)

[0087] f m is the frequency of the nonlinear single frequency deviation, so the LFM frequency domain function containing periodic phase error is:

[0088] f(t)=f c +K r t+ηBcos(2πf m t)

[0089] f c is the carrier frequency, K r is the frequency modulation slope, K r =B / T, T is the pulse width. Then the LFM phase signal when there is periodic frequency deviation distortion is:

[0090]

[0091] make Then the difference frequency signal with periodic phase error is:

[0092] s if (t) = exp{j2π[f c τ+K r tτ+βsin(2πf m t)]}

[0093] The ideal FMCW signal output after passing through the matched filter is s o (t), when there is single-frequency periodic distortion, the output signal after matched filtering is:

[0094]

[0095] Among them J n (β) is the nth-order Bessel function of the first kind, E nv (·) is the de-envelope function.

[0096] It can be seen that when there is a periodic phase error, the signal output after pulse compression is mainly composed of the main signal with an amplitude of J0(β) and the amplitude of J n The time interval and frequency interval between the paired echo and the main signal are ±nf m / K r ±nf m / 2.

[0097] Based on the above derivation, the present application draws the following conclusions: the impact of periodic phase error on radar performance is mainly caused by paired echoes; periodic phase error will affect the detection effect of weak targets near the main lobe and distant targets; periodic phase error will not affect the main lobe width and main lobe position.

[0098] The present application obtains reference difference frequency information containing a periodic phase error, performs phase unwrapping on the reference difference frequency information to obtain phase error information, uses a frequency estimation model and a frequency technology model to make a preliminary estimate of the periodic phase error, thereby obtaining first initial parameter information of the periodic phase error, adopts a phase matching method based on the first initial parameter information to obtain second initial parameter information of the periodic phase error, uses an optimization algorithm to process the first initial parameter information and the second initial parameter information, further optimizes to obtain updated parameter information, and finally corrects the difference frequency information based on the updated parameter information, thereby effectively eliminating the periodic phase error in the difference frequency information, more accurately performing periodic error correction, and improving the application effect of the radar system in complex environments.

[0099] Among them, the error estimation method of the present application is a periodic error estimation method based on a deep network model, which performs polynomial error correction to obtain the phase signal of the difference frequency signal; obtains the initial value of the periodic error through a deep learning model and a phase matching method; uses the ADAM and LBFGS joint algorithm for optimization; uses the estimated parameters to perform phase error correction through Fourier matching, and finally obtains the corrected difference frequency signal.

[0100] As one implementation method, the specific steps of obtaining reference difference frequency information, performing phase unwrapping on the reference difference frequency information, and obtaining phase error information are as follows:

[0101] After the polynomial error correction, the signal containing periodic error is removed from the polynomial phase:

[0102]

[0103] Take this signal for phase unwrapping and get:

[0104]

[0105] Further simplifying this signal yields:

[0106]

[0107] In the formula, a k =2πA k τ ref ; ζ=2πf c τ ref , considering ζ as a constant will not affect the estimation of subsequent sinusoidal parameters, and performing DC removal processing before fitting will eliminate ζ.

[0108] As one implementation mode, the specific steps of performing an initial estimation on the phase error information according to the pre-built frequency estimation model and the frequency counting model to obtain the first initial parameter information are as follows:

[0109] According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

[0110] Specifically, the first initial parameter information of the embodiment of the present application includes initial frequency value information. A model suitable for estimating periodic phase errors, namely a frequency estimation module, is trained using simulation data to convert the input signal into a frequency representation to achieve the purpose of extracting frequency features in the signal. The module includes an input layer, multiple convolutional layers and an output layer, and uses a narrow Gaussian kernel to generate an ideal frequency representation.

[0111] The input layer of the frequency estimation model is: the input signal undergoes a preliminary feature transformation through a linear layer. The function of the linear layer is to project the input signal into an internal representation space with a higher dimension, providing rich feature representation for subsequent convolution operations. The powerful mapping capability of the fully connected layer is utilized to fully extract the initial features of the signal.

[0112] The convolution layer of the frequency estimation model is that the output of the input layer is processed through multiple one-dimensional convolution layers. The convolution layer comes from the successful application of convolutional neural networks in image processing. Its main advantage is that it can effectively capture local features in the signal. Each convolution layer uses multiple filters, and the batch normalization layer and ReLU activation function are followed by the convolution operation. The convolution kernel slides on the signal to generate a feature map, which can capture frequency information of different scales and extract the local frequency features of the signal through the convolution operation. The batch normalization layer helps to speed up the training process and stabilize the training of the model to prevent the gradient from disappearing or exploding. The above structure enables the convolution layer to extract rich frequency features while ensuring computational efficiency, and refine the high-level features of the signal layer by layer through multi-layer stacking.

[0113] The output layer of the frequency estimation model is a deconvolution layer, i.e., a transposed convolution layer, which upsamples the internal representation to the required frequency representation size. The deconvolution layer is a special operation in convolutional neural networks that acts in the opposite way to the convolution operation. Convolution operations are usually used for downsampling, i.e., extracting features and reducing the data size through a sliding window. Deconvolution operations are used for upsampling, i.e., restoring the data size through interpolation and convolution kernel expansion. Through the upsampling of the deconvolution layer, the output frequency representation has a higher detail retention capability, which is helpful for subsequent frequency estimation and counting tasks. High-resolution frequency representation can more accurately capture subtle frequency changes in the signal, thereby improving the accuracy of frequency estimation.

[0114] The embodiment of the present application uses a narrow-band Gaussian kernel to generate ideal frequency information when training the model, specifically: generating a frequency representation, first performing a Fourier transform on the input signal to obtain a frequency domain representation; a convolution operation, then convolving the frequency domain representation with a narrow Gaussian kernel to generate an ideal frequency representation. This step is usually performed in the target generation phase of model training to generate a target frequency representation for training; smoothing, through the convolution operation of the narrow-band Gaussian kernel, each frequency component forms a peak in the frequency representation, highlighting the significance of the frequency component, and suppressing noise and other interference components.

[0115] The loss function uses the mean square error loss function, Mean Squared Error, MSE, which is used to measure the difference between the predicted frequency representation and the true frequency representation:

[0116]

[0117] The MSE loss function can effectively reflect the average deviation between the predicted value and the true value. Through the structural design of the embodiment of the present application, the frequency representation module can effectively extract frequency features from the input signal and generate a high-resolution frequency representation. This architecture design can not only capture the key frequency components in the signal, but also adapt to various noise and interference conditions, thereby improving the accuracy and robustness of frequency estimation. The application of narrow Gaussian kernel further enhances the quality of frequency representation, so that the frequency estimation task performs well in high noise and complex signal environments.

[0118] As one implementation mode, the specific steps of performing an initial estimation on the phase error information according to the pre-built frequency estimation model and the frequency counting model to obtain the first initial parameter information are as follows:

[0119] According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

[0120] Specifically, the first initial parameter information of the embodiment of the present application also includes frequency number information. The frequency counting module of the embodiment of the present application is similar to the frequency estimation module, and the frequency counting module extracts the number of frequencies in the signal from the frequency representation. The module also includes an input layer, multiple convolutional layers and an output layer. The input layer and the convolutional layer are the same as the frequency estimation module. The output of the frequency counting module uses a linear layer to convert the internal representation into the final frequency counting result.

[0121] When the number of frequencies is a discrete value and the possible number of frequencies is finite and known in advance, the model adjusts the loss function to the cross entropy loss function:

[0122]

[0123] As one implementation method, the phase matching function information is:

[0124]

[0125] in, is the initial frequency value information, and K is the frequency number information.

[0126] Specifically, the embodiment of the present application obtains the initial frequency value through frequency estimation and frequency counting model. And the number of frequencies K. Use the estimated frequency parameters to construct the phase matching function, and multiply the phase matching function with the phase error signal to obtain:

[0127]

[0128] As one implementation manner, the specific steps of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are:

[0129] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0130] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0131] Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

[0132] Specifically, when At this time, the DC component is By traversing θ at (-π,π), when the DC component reaches the maximum value, the phase parameter of the frequency component can be obtained.

[0133] As one implementation manner, the specific step of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information further includes:

[0134] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0135] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0136] Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

[0137] Specifically, in the embodiment of the present application, when the DC component takes the maximum value, the phase parameter of the frequency component is obtained, and half of the DC component is the amplitude parameter of the frequency component. Therefore, only one-dimensional search is required to obtain Let k = k + 1, and update the error phase:

[0138]

[0139] The embodiment of the present application repeats the above phase matching process to obtain the parameter values ​​of K sinusoidal components and obtain the initial parameter value Initial value used for subsequent optimization algorithms.

[0140] As one implementation mode, it also includes:

[0141] The first optimization process is a rough optimization performed by using the Adam algorithm;

[0142] The second optimization process is to perform fine optimization using the L-BFGS algorithm.

[0143] Specifically, the Adam optimization algorithm used in the embodiment of the present application is more effective and more robust than the traditional gradient descent method such as SGD in optimization, and can achieve better performance in fewer iterations, but it cannot always find the global optimal solution. Therefore, it is necessary to combine the comprehensive use of other different optimizers to obtain better optimization effects. Therefore, the embodiment of the present application uses L-BFGS for fine optimization.

[0144] Among them, the Adam loss function is the mean square error:

[0145]

[0146] Among them, θ represents the optimization parameter a i ,ω i ,φ i ,y ture is the input data to be fitted.

[0147] In the embodiment of the present application, a K value is initially set. If there is no historical experience information, it starts from 1. There will be subsequent K value training. The initial sine parameters are given in the previous section and are recorded as

[0148] Initialize the first-order moment estimation momentum variable m0=0 and the second-order moment estimation root mean square variable v0=0 as zero vectors, set the learning rate α, the first-order momentum hyperparameter β1, the second-order momentum hyperparameter β2, β1 is usually close to 1, such as 0.9, β2 is usually close to 1, such as 0.999, and a small constant ε=10 to prevent division by zero errors -8 .

[0149] At each iteration, the gradient of the current parameters is calculated:

[0150]

[0151] Among them, θ represents the three parameters of amplitude, frequency and phase a i ,ωi ,φ i , therefore, the gradient of the respective parameters is:

[0152]

[0153] Update the first-order moment estimate, i.e. the momentum variable:

[0154] m t =β1m t-1 +(1-β1)g t

[0155] Where m t is the momentum variable at the current moment, representing the exponentially weighted average of the gradient. Update the second-order moment estimate, i.e., the root mean square variable:

[0156]

[0157] where v t is the root mean square variable at the current moment, representing the exponentially weighted average of the squared gradient. Next, the bias correction is calculated, since m t and v t Initialized to zero vector, so there is deviation in the initial stage, the deviation correction is performed as follows:

[0158]

[0159] Then use the corrected moment estimates to update the parameters:

[0160]

[0161] Among them, θ represents the optimization parameter a i ,ω i ,φ i , the roughly optimized periodic phase error parameter can be obtained, and then this parameter is used for local optimization to obtain a more accurate error parameter.

[0162] In the second optimization process of the embodiment of the present application, that is, in the second stage of optimization, the L-BFGS optimization algorithm is used to further fine-tune the model parameters and improve the fitting accuracy. L-BFGS is a quasi-Newton method that deals with optimization problems requiring high-precision convergence. It approximates the inverse of the Hessian matrix through limited memory, making it more efficient in large-scale optimization problems. In the embodiment of the present application, the L-BFGS algorithm is used to optimize the amplitude, frequency, and phase parameters so that the generated signal matches the real signal as closely as possible. Through gradient calculation, historical record update, search direction calculation, and parameter update, the L-BFGS algorithm gradually optimizes the model parameters to achieve accurate fitting of the signal. Specifically:

[0163] The initialized fitting parameter θ0 is the final result of Adam optimization Initialize a set of history records {S j , G j} is used to store gradients and position vectors, and the immediate record size is set to 20.

[0164] The method for iteratively updating the calculation of the gradient g in the embodiment of the present application is similar to Adam and will not be described in detail. Then the position vector difference and the gradient difference are updated to record the updated historical information, and the inverse of the Hessian matrix is ​​approximately calculated using the historical information:

[0165] S i =θ j+1 -θ j

[0166] G j =g j+1 -g j

[0167] Then calculate the auxiliary variable ρ j :

[0168]

[0169] Initialization vector q = g j The first loop starts from the most recent history and traverses forward, computing the scalar η j And update the vector q:

[0170]

[0171] q=q-η j G j

[0172] Then calculate the scaling factor γ of the initial matrix j And initialize the vector r:

[0173]

[0174] r = γ j q

[0175] The second loop starts from the oldest historical record and traverses backwards, computing the scalar χ j And update the vector r:

[0176]

[0177] r=r+S j (η j -x j )

[0178] From the above, we can get the search direction p j, determine the direction of parameter update so that the loss function decreases fastest along this direction:

[0179] p j =-r

[0180] Finally, update the parameters to get:

[0181] θ j+1 =θ j +χ j p j

[0182] If the norm of the gradient ||g j || is less than the set tolerance value ζ, or reaches the maximum number of iterations, the iteration is stopped and the precise optimization process is completed.

[0183] In the training process of the multi-sine signal model in the embodiment of the present application, the time and signal data are first read, and the data is preprocessed to remove the boundary data points. Then, the initial amplitude, frequency and phase parameters are obtained through the neural network model and phase matching to initialize the neural network model. The model uses the mean square error MSE as the loss function and is preliminarily trained through the Adam optimizer, and the optimal parameter settings are recorded in the process. The model parameters are further optimized by the L-BFGS optimizer, and the fitting accuracy is improved by the quasi-Newton method. In order to further fine-tune the parameters, the least squares method is used for final optimization, and the optimal amplitude, frequency and phase parameters are finally returned for generating the fitting signal, and the results are evaluated and visualized. The comprehensive training process ensures efficient optimization and accurate fitting of the model.

[0184] As one of the implementation methods, the reference difference frequency information is corrected according to the updated parameter information to obtain the correction result information, and the step is to use a more widely applicable matching Fourier transform to achieve multi-target nonlinear correction, and when a nonlinear analytical expression is obtained, the multi-target can be focused. The general form of the specific MFT is:

[0185]

[0186] The multi-target difference frequency signal containing sinusoidal periodic phase error is expressed as:

[0187]

[0188] Where Q is the target number; τ q is the echo delay of the qth target;

[0189] The Fourier transform is:

[0190]

[0191] The embodiment of this application Then if The MFT of (t) is:

[0192]

[0193] Where ψ′(t) is the derivative of ψ(t), and its expression is:

[0194]

[0195] Point targets at different distances are focused as an ideal sinc function, and the frequency point where the target is located is f p =K r τ p The real distance of the target is reflected and the nonlinearity of the system is corrected.

[0196] The period amplitude of the new time axis ψ(t) is:

[0197]

[0198] From the time axis t to the new time axis ψ(t) is the resampling process, the intermediate frequency signal s if The MFT of (t) is equivalent to the Fourier transform on the new time axis ψ(t), and the one-dimensional range image compression result on the new time axis ψ(t) is the compression result after the periodic phase error correction.

[0199] Reference Figure 2 , a radar link periodic error correction system is provided for an embodiment of the present application, comprising:

[0200] An information acquisition module is used to acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information;

[0201] A first initial parameter analysis module, used to perform an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information;

[0202] A second initial parameter analysis module is used to construct phase matching function information according to the first initial parameter information, and analyze the phase matching function information and the phase error information to obtain second initial parameter information;

[0203] An update parameter analysis module is used to perform a first optimization process according to the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain update parameter information;

[0204] The correction module is used to correct the difference frequency information according to the updated parameter information to obtain correction result information.

[0205] As one implementation manner, the first initial parameter analysis module performs an initial estimation on the phase error information according to the pre-built frequency estimation model and the frequency counting model, and the specific steps of obtaining the first initial parameter information are as follows:

[0206] According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

[0207] As one implementation manner, the first initial parameter analysis module performs an initial estimation on the phase error information according to the pre-built frequency estimation model and the frequency counting model, and the specific steps of obtaining the first initial parameter information are as follows:

[0208] According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

[0209] As one implementation manner, the phase matching function information of the second initial parameter analysis module is:

[0210]

[0211] in, is the initial frequency value information, and K is the frequency number information.

[0212] As one implementation manner, the specific steps of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are:

[0213] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0214] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0215] Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

[0216] As one implementation manner, the specific step of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information further includes:

[0217] Multiplying the phase matching function information and the phase error information to obtain DC component information;

[0218] Performing maximum value analysis on the DC component information to obtain the DC component maximum value information;

[0219] Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

[0220] As one implementation mode, it also includes:

[0221] The first optimization process of the update parameter analysis module is to perform rough optimization through the Adam algorithm;

[0222] The second optimization process of the update parameter analysis module is to perform fine optimization through the L-BFGS algorithm.

[0223] An embodiment of the present application provides a radar link periodic error correction device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the radar link periodic error correction method as described above.

[0224] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the radar link periodic error correction method as described above when running.

[0225] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and product can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0226] In several embodiments provided in this application, it should be understood that the disclosed methods, systems, devices and program products may be implemented in other ways.

[0227] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0228] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A radar link periodic error correction method, characterized in that: include: Acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information; Performing an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information; Constructing phase matching function information according to the first initial parameter information, and analyzing the phase matching function information and the phase error information to obtain second initial parameter information; Perform a first optimization process on the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain updated parameter information; The difference frequency information is corrected according to the updated parameter information to obtain correction result information.

2. The radar link periodic error correction method according to claim 1, characterized in that: The specific steps of performing initial estimation of the phase error information according to the pre-built frequency estimation model and the frequency counting model to obtain the first initial parameter information are: According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

3. The radar link periodic error correction method according to claim 2, characterized in that: The specific steps of performing initial estimation of the phase error information according to the pre-built frequency estimation model and the frequency counting model to obtain the first initial parameter information are: According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

4. The radar link periodic error correction method according to claim 3, characterized in that: The phase matching function information is: in, is the initial frequency value information, and K is the frequency number information.

5. The radar link periodic error correction method according to claim 4, characterized in that: The specific steps of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are: Multiplying the phase matching function information and the phase error information to obtain DC component information; Performing maximum value analysis on the DC component information to obtain the DC component maximum value information; Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

6. The radar link periodic error correction method according to claim 5, characterized in that: The specific step of analyzing the phase matching function information and the phase error information to obtain the second initial parameter information also includes: Multiplying the phase matching function information and the phase error information to obtain DC component information; Performing maximum value analysis on the DC component information to obtain the DC component maximum value information; Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

7. The radar link periodic error correction method according to claim 1, characterized in that: Also includes: The first optimization process is a rough optimization performed by using the Adam algorithm; The second optimization process is to perform fine optimization using the L-BFGS algorithm.

8. A radar link periodic error correction system, characterized in that: include: An information acquisition module is used to acquire reference difference frequency information, perform phase unwrapping on the reference difference frequency information, and obtain phase error information; A first initial parameter analysis module, used to perform an initial estimation on the phase error information according to a pre-built frequency estimation model and a frequency counting model to obtain first initial parameter information; A second initial parameter analysis module is used to construct phase matching function information according to the first initial parameter information, and analyze the phase matching function information and the phase error information to obtain second initial parameter information; An update parameter analysis module is used to perform a first optimization process according to the first initial parameter information and the second initial parameter information to obtain rough optimization result information, and perform a second optimization process on the rough optimization result information to obtain update parameter information; The correction module is used to correct the difference frequency information according to the updated parameter information to obtain correction result information.

9. The radar link periodic error correction system according to claim 8, characterized in that: The first initial parameter analysis module performs an initial estimation on the phase error information according to the pre-built frequency estimation model and frequency counting model, and the specific steps of obtaining the first initial parameter information are: According to the pre-built frequency estimation model, the frequency value of the phase error information is estimated to obtain initial frequency value information.

10. The radar link periodic error correction system according to claim 9, characterized in that: The first initial parameter analysis module performs an initial estimation on the phase error information according to the pre-built frequency estimation model and frequency counting model, and the specific steps of obtaining the first initial parameter information are: According to the pre-built frequency counting model, the frequency number of the phase error information is estimated to obtain the frequency number information.

11. The radar link periodic error correction system according to claim 10, characterized in that: The phase matching function information of the second initial parameter analysis module is: in, is the initial frequency value information, and K is the frequency number information.

12. The radar link periodic error correction system according to claim 11, characterized in that: The specific steps of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information are: Multiplying the phase matching function information and the phase error information to obtain DC component information; Performing maximum value analysis on the DC component information to obtain the DC component maximum value information; Phase analysis is performed based on the maximum value information of the DC component to obtain initial phase parameter information.

13. The radar link periodic error correction system according to claim 12, characterized in that: The specific step of the second initial parameter analysis module analyzing the phase matching function information and the phase error information to obtain the second initial parameter information also includes: Multiplying the phase matching function information and the phase error information to obtain DC component information; Performing maximum value analysis on the DC component information to obtain the DC component maximum value information; Amplitude analysis is performed based on the maximum value information of the DC component to obtain initial amplitude parameter information.

14. The radar link periodic error correction system according to claim 8, characterized in that: Also includes: The first optimization process of the update parameter analysis module is to perform rough optimization through the Adam algorithm; The second optimization process of the update parameter analysis module is to perform fine optimization through the L-BFGS algorithm.

15. A radar link periodic error correction device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the radar link periodic error correction method according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the radar link periodic error correction method according to any one of claims 1 to 7 when running.

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