Beidou satellite signal acquisition method and device

Abstract

The application relates to the technical field of signal processing, in particular to a Beidou satellite signal acquisition method and device, wherein the method comprises the following steps: acquiring a target satellite signal; calculating a covariance matrix of the target satellite signal; acquiring a target hyperparameter corresponding to the target satellite signal; constructing a propagator according to the covariance matrix and the target hyperparameter; and calculating a frequency matrix and a phase matrix of the target satellite signal according to the propagator, so that the number of Beidou satellite signals does not need to be known in advance, the frequency and the phase of the Beidou satellite signals can also be calculated, the acquisition of the Beidou satellite signals is realized, the problem of poor acquisition performance of the existing Beidou satellite signals is solved, and the acquisition performance of the Beidou satellite signals is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a Beidou satellite signal acquisition method and device. BACKGROUND

[0002] Satellite navigation system can provide high-precision, all-weather positioning, navigation and timing services, and is an information resource that can be shared by military and civilian users in sea, land and air fields. The Beidou satellite navigation and positioning system (hereinafter referred to as the Beidou system) developed by China has been in stable operation since April 2004, and has been fully opened to civilian users. So far, the system has played an important role in many fields such as surveying and mapping, telecommunications, water conservancy, transportation, exploration and national security, and has brought great convenience to people's life. In order to use Beidou satellite signals for positioning and navigation, Beidou satellite signal acquisition must be realized first. The performance of Beidou satellite signal acquisition method will directly affect the performance of positioning and navigation, so using a good Beidou satellite signal acquisition method is very important for the application of Beidou satellite.

[0003] The traditional Beidou satellite signal acquisition method includes time domain serial acquisition method and parallel acquisition algorithm based on fast Fourier transform. The time domain serial acquisition method is a two-dimensional search for code phase and Doppler frequency of the signal received by the receiver, which has a very large amount of calculation and takes a long time to calculate, and cannot meet the real-time requirements. The parallel acquisition algorithm based on fast Fourier transform improves the real-time performance of the time domain serial acquisition algorithm, but still has some shortcomings, such as the need for a large amount of calculation and storage space, the limitation of signal sampling rate and signal length, and the long search time in the frequency band far from the center frequency.

[0004] Moreover, the existing Beidou satellite signal acquisition method usually needs to know the number of received Beidou satellite signals in advance, otherwise satellite signal acquisition cannot be performed, which is almost impossible to achieve in real application scenarios. In order to solve such problems, the existing method estimates the number of received Beidou satellite signals, but this has a huge risk, because if the signal number estimation is wrong, the Beidou satellite signal acquisition cannot be completed correctly, so that the navigation and positioning work cannot be carried out normally. Therefore, how to improve the performance of Beidou satellite signal acquisition has become a problem to be solved. SUMMARY

[0005] The present application provides a Beidou satellite signal acquisition method and device, which solves the technical problem of poor performance of the existing Beidou satellite signal acquisition.

[0006] In one aspect, the present application provides a Beidou satellite signal acquisition method, which comprises:

[0007] obtaining a target satellite signal;

[0008] calculating a covariance matrix of the target satellite signal;

[0009] obtaining a target hyperparameter corresponding to the target satellite signal;

[0010] constructing a propagator according to the covariance matrix and the target hyperparameter;

[0011] calculating a frequency matrix and a phase matrix of the target satellite signal according to the propagator.

[0012] Optionally, the obtaining a target satellite signal comprises:

[0013] obtaining an initial Beidou satellite signal;

[0014] extracting a first target signal feature from the initial Beidou satellite signal;

[0015] inputting the first target signal feature into a pre-trained denoising model to output the target satellite signal.

[0016] Optionally, the obtaining a target hyperparameter corresponding to the target satellite signal comprises:

[0017] extracting a second target signal feature from the target satellite signal;

[0018] inputting the second target signal feature into a pre-constructed regression model to output the target hyperparameter.

[0019] Optionally, the propagator is:

[0020] U im = I M×M - R(R H R + τI M×M ) -1 R H

[0021] U im is a propagator, I M×M is a unit matrix, R is a covariance matrix, τ is a target hyperparameter, and H represents Hermitian transpose.

[0022] Optionally, the calculating a frequency matrix and a phase matrix of the target satellite signal according to the propagator comprises:

[0023] constructing a spatial spectrum function according to the propagator;

[0024] traversing the spatial spectrum function to determine a spectrum peak of the spatial spectrum function, and determining the frequency matrix of the target satellite signal according to the spectrum peak.

[0025] According to the frequency matrix of the target satellite signal, a pseudo-inverse matrix of the frequency matrix is calculated;

[0026] The pseudo-inverse matrix and the target satellite signal are subjected to least squares to obtain a target column vector;

[0027] The target column vector is solved to obtain a phase matrix of the target satellite signal.

[0028] Optionally, the training step of the noise reduction model comprises:

[0029] Obtaining original data and noise-free signal data; the original data is signal data fused with a Beidou satellite signal and background noise;

[0030] The original data is subjected to data preprocessing to obtain an original data sequence, and original signal features are extracted from the original data sequence;

[0031] A recurrent neural network model is constructed;

[0032] The original signal features are taken as inputs of the recurrent neural network model, the noise-free signal data is taken as outputs of the recurrent neural network model, the recurrent neural network model is trained, and the noise reduction model is obtained.

[0033] Optionally, the calculation of the covariance matrix of the target satellite signal comprises:

[0034] The target satellite signal is subjected to signal frequency sampling to obtain a blind separation model corresponding to the target satellite signal;

[0035] The blind separation model is subjected to autocorrelation matrix calculation operation to obtain the covariance matrix of the target satellite signal.

[0036] Optionally, the training step of the regression model comprises:

[0037] An experimental data set is obtained, which comprises sample signal features of noise-free signal data and hyperparameter experimental values;

[0038] An initial regression model is constructed, the sample signal features are taken as inputs of the initial regression model, the hyperparameter experimental values are taken as outputs of the initial regression model, and a support vector regression method is used to train the initial regression model to obtain optimal model parameters of the initial regression model;

[0039] The regression model is constructed according to the optimal model parameters and the initial regression model.

[0040] Optionally, the blind separation model is:

[0041] x = B · S + n

[0042] x = [x(1), x(2), … x(M)] T

[0043] x ∈ V M×1

[0044] B = [b1, b2, … b N ]

[0045] B ∈ V M×N

[0046]

[0047] b k ∈ V M×1

[0048] k ∈ {1, 2, …, N}

[0049]

[0050] S ∈ V N×1

[0051] n = [n(1), n(2), … n(M)] T

[0052] n ∈ V M×1

[0053] F k = f IF + f k

[0054] Wherein, V represents a complex space, T represents a transpose, H represents a Hermitian transpose; x represents a Beidou satellite signal collected by different channels at the same sampling point, M represents a number of sampling channels; tg represents a sampling number; A k represents an amplitude of a received kth Beidou satellite signal; θ k,0 represents an initial phase of the received kth Beidou satellite signal; P k represents a product of a ranging code and a navigation message code of the received kth Beidou satellite signal; F k is a frequency of the kth Beidou satellite signal; f IF is an intermediate frequency carrier frequency, f k and θ k , 0 respectively represent a frequency and an initial phase of the kth Beidou satellite signal collected by a receiver, n(t is background noise at t time;

[0055] A calculation formula of a covariance matrix of the target satellite signal is as follows:

[0056] R = E (x * x H ) = B * E (S * S H ) * B H + sigma 2 I = B * R xx * B H + sigma 2 I

[0057] Wherein, R xx is the covariance matrix of the satellite signal sent by the Beidou satellite, I is the unit matrix, E is the mathematical expectation, R is the covariance matrix of the target satellite signal received by the receiver.

[0058] Another aspect of the present application provides a Beidou satellite signal acquisition device, the device comprises:

[0059] The first acquisition module is used for acquiring the target satellite signal.

[0060] The first calculation module is used for calculating the covariance matrix of the target satellite signal.

[0061] The second acquisition module is used for acquiring the target hyperparameter corresponding to the target satellite signal.

[0062] The construction module is used for constructing the propagator according to the covariance matrix and the target hyperparameter.

[0063] The second calculation module is used for calculating the frequency matrix and the phase matrix of the target satellite signal according to the propagator.

[0064] From the above technical solutions, the present application has the following advantages:

[0065] The present application provides a Beidou satellite signal acquisition method, comprising acquiring the target satellite signal; calculating the covariance matrix of the target satellite signal; acquiring the target hyperparameter corresponding to the target satellite signal; constructing the propagator according to the covariance matrix and the target hyperparameter; and calculating the frequency matrix and the phase matrix of the target satellite signal according to the propagator.

[0066] In the application, the preliminary screening of the Beidou satellite signal is realized by acquiring the target satellite signal after noise reduction, and the acquisition accuracy of the Beidou satellite signal is improved; and the covariance matrix of the target satellite signal is calculated to acquire the target hyperparameter corresponding to the target satellite signal, the propagator is constructed according to the covariance matrix and the target hyperparameter, and the construction of the propagator without using the number of Beidou satellite signals is realized; and the frequency matrix and the phase matrix of the target satellite signal are calculated by using the propagator, so that the frequency and the phase of the Beidou satellite signal can be calculated without knowing the number of Beidou satellite signals in advance, the acquisition of the Beidou satellite signal is realized, the problem of poor acquisition performance of the existing Beidou satellite signal is solved, and the acquisition performance of the Beidou satellite signal is improved. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0068] Figure 1 A flowchart of a Beidou satellite signal acquisition method provided by an embodiment of the present application;

[0069] Figure 2 A Beidou satellite signal receiving principle diagram provided by an embodiment of the present application;

[0070] Figure 3 Another flowchart of a Beidou satellite signal acquisition method provided by an embodiment of the present application;

[0071] Figure 4 A structure diagram of a noise reduction model provided by an embodiment of the present application;

[0072] Figure 5 A structure diagram of a regression model provided by an embodiment of the present application;

[0073] Figure 6 Still another flowchart of a Beidou satellite signal acquisition method provided by an application example of the present application;

[0074] Figure 7 A structure diagram of a Beidou satellite signal acquisition device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0075] In the existing Beidou satellite signal acquisition process, the eigenvalue decomposition method is usually used to obtain the noise subspace, which will produce huge calculation amount, long calculation time and poor real-time performance. Moreover, this method requires prior knowledge of the number of Beidou satellite signals received by the receiver, which is almost impossible to achieve in real application scenarios. In order to solve this problem, the most common solution in existing technologies is to estimate the number of Beidou satellite signals received by the receiver, but this has a huge risk. If the estimated number is inaccurate, the correct noise subspace cannot be obtained, and the spatial spectrum function cannot be correctly constructed, so the Beidou satellite signal acquisition cannot be completed.

[0076] Therefore, the embodiments of the present application provide a Beidou satellite signal acquisition method and device, which solve the technical problem of poor Beidou satellite signal acquisition performance.

[0077] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0078] Please refer to Figure 1 The embodiments of the present application provide a Beidou satellite signal acquisition method, which comprises:

[0079] 101, obtaining a target satellite signal.

[0080] It should be noted that the target satellite signal refers to the Beidou satellite signal obtained after the Beidou satellite signal received by the receiver is denoised.

[0081] 102, calculating the covariance matrix of the target satellite signal.

[0082] It should be noted that the calculation process of the present embodiment is to convert the target satellite signal into a blind separation model, and to solve the autocorrelation matrix of the blind separation model, so as to obtain the covariance matrix of the target satellite signal.

[0083] 103, obtaining a target hyperparameter corresponding to the target satellite signal.

[0084] It should be noted that there is an association relationship between the target satellite signal and the target hyperparameter, and the present embodiment determines the corresponding target hyperparameter according to the target satellite signal and the association relationship.

[0085] 104, constructing a propagator according to the covariance matrix and the target hyperparameter.

[0086] It should be noted that if the number K of the Beidou satellite signals received by the receiver is known, it can be used to select a specific column in the covariance matrix R to form a full column rank subspace, construct a propagator, and use the orthogonality of the propagator and the signal direction vector to construct a spatial spectrum function. However, in actual application scenarios, the number of Beidou satellite signals received by the receiver cannot be known in advance. When the number of Beidou satellite signals received by the receiver is unknown, if the number of signals is estimated, when the estimated number of signals is less than the actual number of signals, the rank of the subspace constructed by selecting a specific column in the covariance matrix R is less than K, the propagator and the direction vector do not satisfy the orthogonality, and the spatial spectrum function cannot be correctly constructed. When the estimated number of signals is greater than the actual number of signals K, the subspace constructed at this time is not full rank, and the inversion operation in the propagator cannot be performed because the condition is not met.

[0087] Since the rank of the matrix R is greater than or equal to the number of signals, the embodiment uses the entire matrix of the covariance matrix R to construct the propagator, which can ensure that the propagator and the direction vector are orthogonal. Moreover, the embodiment introduces a hyperparameter to make the matrix R satisfy the inversion condition, solve the problem that the inversion operation in the propagator cannot be performed because the rank of the matrix R is not full. H R is not full rank.

[0088] The constructed propagator has the following form:

[0089] U im =I M×M -R(R H R+τI M×M ) -1 R H

[0090] where τ is a positive hyperparameter, and the optimal value of τ is determined by machine learning.

[0091] By using the parameter τ, the embodiment can ensure that the above propagator satisfies the inversion operation condition, so that the spatial spectrum function can be correctly constructed without knowing the number of Beidou satellite signals received by the receiver, which meets the actual use scenario, avoids eigenvalue decomposition, reduces the amount of calculation, thereby reducing the calculation time, improving the real-time performance of the Beidou satellite signal acquisition, and improving the performance of the Beidou satellite signal acquisition. The existing Beidou satellite signal acquisition method usually needs to perform eigenvalue decomposition in the process of solving the satellite signal frequency, which results in a large amount of calculation in the solving process and a long time consumption, and cannot meet the real-time requirement well.

[0092] 105、According to the propagator, the frequency matrix and the phase matrix of the target satellite signal are calculated.

[0093] It should be noted that the calculation process of the embodiment is to construct a corresponding spatial spectrum function through the propagator, and to obtain the frequency matrix and the phase matrix of the target satellite signal according to the spatial spectrum function. The frequency matrix represents the frequency of the target satellite signal, and the phase matrix represents the initial phase of the target satellite signal. The initial phase and the frequency are both obtained, which means that the acquisition of the Beidou satellite signal is completed.

[0094] In the embodiment, the preliminary screening of the Beidou satellite signal is realized by obtaining the target satellite signal after noise reduction, and the acquisition accuracy of the Beidou satellite signal is improved. The covariance matrix of the target satellite signal is calculated to obtain the target hyperparameter corresponding to the target satellite signal, and the propagator is constructed according to the covariance matrix and the target hyperparameter, so that the construction of the propagator without using the number of Beidou satellite signals is realized. The frequency matrix and the phase matrix of the target satellite signal are calculated by using the propagator, so that the frequency and the phase of the Beidou satellite signal can be calculated without knowing the number of Beidou satellite signals in advance, the acquisition of the Beidou satellite signal is realized, the problem of poor acquisition performance of the existing Beidou satellite signal is solved, and the acquisition performance of the Beidou satellite signal is improved.

[0095] It should be noted that the sequence between step 102 and step 103 is not limited in the embodiment, and step 103 can be performed before step 102, after step 102, or simultaneously with step 102.

[0096] Please refer to Figure 3 The Beidou satellite signal acquisition method provided by the embodiment of the application comprises the following steps:

[0097] 201, obtaining an initial Beidou satellite signal.

[0098] It should be noted that, as Figure 2 shown, in the embodiment, the receiver is used to receive the Beidou satellite signal. The specific form of the Beidou satellite signal received by the receiver can be represented by the following formula:

[0099]

[0100] Wherein, N is the number of received Beidou satellite signals, C k (t) and D k (t) represent the ranging code and the navigation message code of the kth Beidou satellite signal collected by the receiver at time t, A k represents the amplitude of the kth Beidou satellite signal collected by the receiver, f IF is the intermediate frequency carrier frequency, f k and θ k,0respectively represent the frequency and initial phase of the kth Beidou satellite signal collected by the receiver, and n(t) is the background noise at time t, which obeys Gaussian distribution.

[0101] 202、Extract the first target signal feature from the initial Beidou satellite signal.

[0102] 203、Input the first target signal feature into the pre-trained denoising model to output the target satellite signal.

[0103] It should be noted that the first target signal feature refers to a feature that can be used for identification in the initial Beidou satellite signal, such as time domain features and frequency domain features. The time domain features include mean, standard deviation, etc. The frequency domain features include power spectral density.

[0104] The denoising model is pre-constructed and trained. In actual use, the first target signal feature is input into the denoising model, which can well denoise the initial Beidou satellite signal.

[0105] In a specific example, the training steps of the denoising model include:

[0106] S31, Obtain original data and noise-free signal data. The original data is signal data fused with Beidou satellite signals and background noise.

[0107] It should be noted that the original data and noise-free signal data collected in this embodiment contain Beidou satellite signals and background noise. The noise-free signal data refers to clean Beidou satellite signals without background noise, which can be obtained from a simulated environment or by labeling the Beidou satellite signals that meet the requirements.

[0108] It can be understood that the training data set and label used to train the denoising model are prepared in advance in this embodiment, wherein the label is used to label clean noise-free signals.

[0109] S32, data preprocessing is performed on the original data to obtain an original data sequence, and original signal features are extracted from the original data sequence.

[0110] It should be noted that data preprocessing includes removing outliers, normalization operation, data segmentation and feature extraction. Specifically, after removing outliers and normalizing the original data, the normalized original data is sliced into multiple signal sequences, each signal sequence containing consecutive time steps, and then useful features (i.e. original signal features) are extracted from the sliced signal sequences, including time domain features (such as mean, standard deviation) and frequency domain features (such as power spectral density).

[0111] S33, construct a recurrent neural network model.

[0112] It should be noted that the noise reduction model of the embodiment adopts a recurrent neural network (RNN) model, which specifically includes an input layer, a hidden layer, and an output layer. The structure of the recurrent neural network (RNN) model is as shown in Figure 4 At time t, m t represents the feature sequence corresponding to the received signal input at this moment, q t represents the output value of the RNN network hidden layer at the last moment, m t and qt jointly determine the value of the hidden layer at time t. is the value of the output layer at time t, y t is the true value of the training sample sequence at time t, and L is a loss function. U is a weight matrix from the input layer to the hidden layer, V is a weight matrix from the hidden layer to the output layer, and the weight matrix W represents the weight of the last value of the hidden layer as the input this time. The initial values of the weight matrices U, V, and W are set according to experience.

[0113] S34, taking the original signal feature as the input of the recurrent neural network model and taking the noise-free signal data as the output of the recurrent neural network model, training the recurrent neural network model to obtain a noise reduction model.

[0114] It should be noted that the training principle of the recurrent neural network model is as follows:

[0115] At time t, the original signal feature corresponding to the initial Beidou satellite signal is taken as the input m t , and the corresponding noise-free signal y t is taken as the output target, q t is obtained by m t and q t-1 passing through the hidden layer calculation:

[0116] q t =σ(Um t +Wq t-1 +b)

[0117] Where σ is the activation function of RNN, and its form is:

[0118]

[0119] Where b is a bias value vector.

[0120] Then, the output of the RNN at time t is calculated as:

[0121]

[0122] Where c is a bias value vector.

[0123] The loss function is calculated, and the loss function is as follows:

[0124]

[0125] Then, the values of the weight matrix and the bias value vector are updated according to the numerical value of the loss function.

[0126] Wherein, the update of V and c is as follows:

[0127]

[0128] The calculation of W, U and b needs to consider the gradient propagation on the time sequence at the same time, and the hidden layer at time t is defined as:

[0129]

[0130] The gradient update of W, U and b is:

[0131]

[0132] According to the above training principle, iterative update is performed until the optimal model parameters are obtained.

[0133] After that, the trained recurrent neural network model is evaluated and parameter optimized. The cross-validation method is used to evaluate the performance of the trained RNN model, and the root mean square error is used as the evaluation index. According to the model evaluation result, the hyperparameters of the RNN model are adjusted, such as learning rate, number of hidden layer units, iteration times, etc., to optimize the model performance, until the model performance reaches the optimal, so as to obtain the best recurrent neural network model, that is, the denoising model, so that in actual use, the initial Beidou satellite signal received is denoised by the denoising model, and good denoising effect can be achieved.

[0134] Therefore, the embodiment adopts the trained denoising model to denoise the initial Beidou satellite signal, so as to achieve better preprocessing effect, thereby obtaining better Beidou satellite signal acquisition performance, and further improving the acquisition performance of the Beidou satellite signal.

[0135] 204, calculate the covariance matrix of the target satellite signal.

[0136] It should be noted that the target satellite signal obtained after denoising is converted into a blind separation model, and then the covariance matrix of the target satellite signal is calculated according to the blind separation model.

[0137] Step 204 specifically includes the following substeps:

[0138] S41, performing frequency sampling on the target satellite signal to obtain a blind separation model corresponding to the target satellite signal.

[0139] After the receiver collects the Beidou satellite signal, the signal model received by the receiver is converted into a blind separation model through frequency sampling, and the blind separation model is as follows:

[0140] x = B·S + n

[0141] wherein,

[0142] x = [x(1), x(2), … x(M)] T , x ∈ V M×1 .

[0143] B = [b1, b2, … b N ], B ∈ V M×N , wherein b k ∈ V M ×1 , k ∈ {1, 2, …, N}.

[0144] S ∈ V N×1 . n = [n(1), n(2), … n(M)] T , n ∈ V M×1 .

[0145] wherein, V represents a complex space, T represents a transpose, H represents a Hermitian transpose; x represents the Beidou satellite signal collected by different channels at the same sampling point, M represents the number of sampling channels; F k = f IF + f k , t g in the matrix B represents the sampling number; A k , θ k,0 in the column vector S represent the amplitude and initial phase of the received kth Beidou satellite signal respectively; P k represents the product of the ranging code and the navigation message code of the received kth Beidou satellite signal.

[0146] Since the ranging code and the navigation message code are both digital coded signals, it is considered that P is irrelevant to the sampling channel when the sampling is sufficient.

[0147] S42, autocorrelation matrix calculation operation is performed on the blind separation model to obtain the covariance matrix of the target satellite signal.

[0148] It should be noted that according to the blind separation model calculation formula, the autocorrelation matrix is calculated on both sides at the same time, and combined with the frequency invariance characteristic of the sine signal correlation function, the covariance matrix R of the received signal is obtained, and the calculation formula is specifically as follows:

[0149] R = E(x·x H ) = B·E(S·S H )·BH +σ 2 I=B·R xx ·B H +σ 2 I

[0150] wherein, R xx is the covariance matrix of the satellite signal sent by the Beidou satellite, I is the unit matrix, E is the mathematical expectation, and R is the covariance matrix of the target satellite signal received by the receiver.

[0151] 205、extracting a second target signal feature from the target satellite signal, inputting the second target signal feature into a pre-constructed regression model, and outputting a target hyperparameter.

[0152] It should be noted that the training step of the regression model comprises:

[0153] S51, obtaining an experimental data set, the experimental data set comprising sample signal features of noise-free signal data, and hyperparameter experimental values.

[0154] It should be noted that in the training regression model stage, a plurality of simulation experiments are performed in advance, and the features of the signal, the hyperparameters, and the obtained frequency values obtained in each experiment are recorded, so as to obtain an original data set. Then, the original data set is preprocessed to process the repeated values, missing values and abnormal values in the original data set, the data set is split into a training set and a test set, the data set is scaled by a normalization method, and an experimental data set for training an initial regression model is obtained.

[0155] S52, constructing an initial regression model, taking the sample signal features as the input of the initial regression model, taking the hyperparameter experimental values as the output of the initial regression model, and training the initial regression model by using a support vector regression method to obtain the best model parameters of the initial regression model.

[0156] It should be noted that the problem of obtaining the value of the hyperparameter is a linear regression problem, the correlation between variables is found by training, the relationship between variables is determined, and finally the known variables are input to predict the value of the required hyperparameter. The model function used in this embodiment is y = wx + b.

[0157] wherein, y is the hyperparameter to be solved, x is the variable affecting the value of the hyperparameter, such as the signal-to-noise ratio, w is the weight, and b is the bias.

[0158] It can be understood that the purpose of training the initial regression model is to solve the unknown numbers w and b to obtain the specific function of y. When the features of the signal are known, the value of the hyperparameter can be obtained. For the linear regression problem, the goal is to fit a regression line to the data to minimize the error caused by the bias.

[0159] Specifically, the embodiment adopts a support vector regression method (SVR) to train an initial regression model, and sets a threshold error tolerance ε around the regression line, as shown in Figure 5 For samples outside the tolerance range, the loss value is calculated, i.e., the difference value between the predicted value and the true value. After obtaining the loss value, the model updates the parameters through back propagation to reduce the loss between the true value and the predicted value, so that the predicted value generated by the model approaches the true value, thereby achieving the purpose of learning.

[0160] Specifically, the embodiment constructs slack variables ξ i to allow some samples to be outside the interval band, as shown in Figure 5 represents the distance between the projection of the upper edge sample point on the upper edge line and the sample point, and ξ i represents the distance between the projection of the lower edge sample point on the lower edge line and the sample point.

[0161] The SVR optimizes the model by maximizing the tolerance width and minimizing the total loss, and the SVR objective function can be expressed as:

[0162]

[0163] wherein,

[0164]

[0165] and the values of ξ i are:

[0166]

[0167] To minimize the objective function, the embodiment constructs a Lagrange function according to the constraint condition, and takes the partial derivative of w, b, and ξ i , and sets the partial derivative to 0.

[0168] Finally, the embodiment uses a sequential minimal optimization algorithm (SMO) to solve the coefficients w and b of the regression model. At this time, the obtained optimal model parameters are the coefficients w and b.

[0169] S53, according to the optimal model parameters and the initial regression model, a regression model is constructed.

[0170] It should be noted that inputting the optimal model parameters into the initial regression model can obtain the regression model required by the embodiment.

[0171] In actual use, inputting the extracted second target signal features into the regression model can obtain the optimal hyperparameters, i.e., the target hyperparameters.​

[0172] It can be understood that the second target signal feature is consistent with the signal feature used for training the initial regression model, which can be a signal-to-noise ratio.

[0173] 206. Constructing a propagator according to the covariance matrix and the target hyperparameter.

[0174] It should be noted that step 206 can refer to step 104, and the present embodiment will not be repeated here.

[0175] 207. Calculating the frequency matrix and the phase matrix of the target satellite signal according to the propagator.

[0176] It should be noted that step 207 specifically includes the following sub-steps:

[0177] S71. Constructing a spatial spectrum function according to the propagator.

[0178] It should be noted that the propagator U im is used to construct the following spatial spectrum function:

[0179]

[0180] Where G(f) is the spatial spectrum function.

[0181] S72. Traversing the spatial spectrum function to determine the spectral peak of the spatial spectrum function, and determining the frequency matrix of the target satellite signal according to the spectral peak.

[0182] It should be noted that the present embodiment determines the spectral peak in the preset frequency range by traversing the spatial spectrum in the preset frequency range, and the frequency corresponding to the spectral peak is the frequency of the target satellite signal, wherein the frequency is in the form of a matrix, i.e. the frequency matrix is obtained.

[0183] Where the preset frequency range refers to the range of Beidou satellite signal civilian frequencies specified in the official document of Beidou satellite navigation system.

[0184] S73. Calculating the pseudo-inverse matrix of the frequency matrix according to the frequency matrix of the target satellite signal.

[0185] S74. Performing least squares on the pseudo-inverse matrix and the target satellite signal to obtain a target column vector.

[0186] S75. Solving the target column vector to obtain the phase matrix of the target satellite signal.

[0187] It should be noted that the initial phase of the received Beidou satellite signal can be directly estimated according to the obtained frequency of the received Beidou satellite signal. Specifically, after the frequency matrix B is calculated, the pseudo-inverse matrix B' is calculated, and the pseudo-inverse matrix B' is used to perform linear least squares on the denoised satellite signal to obtain:

[0188] S = B' x

[0189] where B' is the pseudo-inverse matrix of the frequency matrix B. Then the initial phase of the Beidou satellite signal can be solved as follows:

[0190]

[0191] where θ = [θ 1,0 , θ 2,0 , … θ N,0 T , θ ∈ V N×1 .

[0192] As can be seen from the above, the frequency and initial phase of the received initial Beidou satellite signal are obtained, that is, the acquisition of the Beidou satellite signal is completed.

[0193] In a specific application example, the present application can be implemented according to the process steps indicated in the specific implementation process. The process specifically includes starting, collecting Beidou satellite signals; determining the specific form of the signal; using a recurrent neural network algorithm to denoise the signal; determining a blind separation model of the Beidou satellite signal in combination with the blind separation model; obtaining the covariance matrix of the received signal; introducing a hyperparameter to construct a propagator without using the number of signals; constructing a spatial spectrum function, traversing the frequency to obtain the satellite signal frequency; combining the obtained signal frequency to solve the initial phase of the satellite signal, and ending. Figure 6

[0194] wherein the stage of denoising the signal using a recurrent neural network algorithm includes: data acquisition and preprocessing; data segmentation and labeling, feature extraction and conversion in the signal sequence; constructing an RNN model and performing model training; using root mean square error as an index to evaluate the model; parameter tuning to optimize the model performance.

[0195] wherein the stage of introducing a hyperparameter to construct a propagator without using the number of signals includes repeating experiments, recording data, and obtaining an original data set; preprocessing the data; determining the form of the model function; training and optimizing the model using a support vector regression method; solving the specific form of the model function to obtain the value of the hyperparameter.

[0196] ​As can be seen from the above, the application solves the problem that the prior Beidou satellite acquisition algorithm needs to know the number of received Beidou satellite signals in advance, avoids estimating the number of signals, and fundamentally solves the huge risk caused by inaccurate signal number estimation. At the same time, the application solves the problem that the prior Beidou satellite signal acquisition method needs to perform eigenvalue decomposition in the process of solving the satellite signal frequency, thereby generating a large amount of calculation and a long calculation time, and hopes to obtain better real-time performance. In addition, the application improves the preprocessing process of the prior Beidou signal acquisition method by combining a high-performance neural network algorithm and a machine learning algorithm, and obtains better Beidou satellite signal acquisition performance.

[0197] The application constructs a propagator without using the number of Beidou satellite signals, instead of the traditional method of using eigenvalue decomposition to solve the noise subspace, uses the propagator of the application to construct a spatial spectrum function, performs spatial spectrum search, and can obtain the frequency of the Beidou satellite signal to be solved under the condition that the number of Beidou satellite signals is unknown. Then, the signal phase is solved according to the obtained satellite signal frequency, and the acquisition of the Beidou satellite signal is completed. Therefore, the application does not need to know the number of Beidou satellite signals to be acquired in advance, which is more in line with the real use scenario and is not constrained by prior knowledge of the number of signals. At the same time, since the application avoids eigenvalue decomposition, the amount of calculation can be significantly reduced, thereby reducing the calculation time and obtaining better real-time performance, which is more conducive to application in real scenarios. In addition, the application uses a high-performance neural network algorithm in the signal preprocessing module and uses a machine learning algorithm in the hyperparameter value module, which can achieve better Beidou satellite signal acquisition effect and obtain better acquisition performance,

[0198] Referring to Figure 6 The application embodiment provides a Beidou satellite signal acquisition device, which comprises:

[0199] The first acquisition module 601 is configured to acquire a target satellite signal.

[0200] The first calculation module 602 is configured to calculate the covariance matrix of the target satellite signal.

[0201] The second acquisition module 603 is configured to acquire a target hyperparameter corresponding to the target satellite signal.

[0202] The construction module 604 is configured to construct a propagator according to the covariance matrix and the target hyperparameter.

[0203] The second calculation module 605 is configured to calculate the frequency matrix and the phase matrix of the target satellite signal according to the propagator.

[0204] In one specific embodiment, the first acquisition module 601 comprises:

[0205] The first obtaining unit is configured to obtain an initial Beidou satellite signal.

[0206] The first extraction unit is configured to extract a first target signal feature from the initial Beidou satellite signal.

[0207] The target satellite signal output unit is configured to input the first target signal feature into a pre-trained denoising model and output a target satellite signal.

[0208] The first obtaining module 601 further includes a denoising model training unit.

[0209] In one specific embodiment, the denoising model training unit includes:

[0210] The first obtaining subunit is configured to obtain raw data and noise-free signal data; the raw data is signal data fused with a Beidou satellite signal and background noise.

[0211] The first extraction subunit is configured to perform data preprocessing on the raw data to obtain a raw data sequence and extract a raw signal feature from the raw data sequence.

[0212] The first construction subunit is configured to construct a recurrent neural network model.

[0213] The training subunit is configured to take the raw signal feature as an input of the recurrent neural network model, take the noise-free signal data as an output of the recurrent neural network model, train the recurrent neural network model, and obtain a denoising model.

[0214] In one specific embodiment, the first calculation module 602 includes:

[0215] The model conversion unit is configured to perform frequency sampling on the target satellite signal to obtain a blind source separation model corresponding to the target satellite signal.

[0216] The first calculation unit is configured to perform autocorrelation matrix calculation on the blind source separation model to obtain a covariance matrix of the target satellite signal.

[0217] In one specific embodiment, the second obtaining module 603 includes:

[0218] The second extraction unit is configured to extract a second target signal feature from the target satellite signal.

[0219] The target hyperparameter output unit is configured to input the second target signal feature into a pre-constructed regression model and output a target hyperparameter.

[0220] In one specific embodiment, the second obtaining module 603 further includes a regression model training unit,

[0221] The regression model training unit specifically includes:

[0222] a second obtaining subunit, configured to obtain an experimental data set, the experimental data set including sample signal features of noise-free signal data and hyperparameter experimental values;

[0223] a second training subunit, configured to construct an initial regression model, use the sample signal features as input of the initial regression model, use the hyperparameter experimental values as output of the initial regression model, and train the initial regression model by using a support vector regression method to obtain optimal model parameters of the initial regression model;

[0224] a second constructing subunit, configured to construct a regression model according to the optimal model parameters and the initial regression model.

[0225] In one specific embodiment, the second computing module 605 includes:

[0226] a spatial spectrum function constructing unit, configured to construct a spatial spectrum function according to the propagator;

[0227] a frequency matrix determining unit, configured to traverse the spatial spectrum function, determine a spectrum peak of the spatial spectrum function, and determine a frequency matrix of the target satellite signal according to the spectrum peak;

[0228] a pseudo-inverse matrix computing unit, configured to compute a pseudo-inverse matrix of the frequency matrix according to the frequency matrix of the target satellite signal;

[0229] an operation unit, configured to perform least squares between the pseudo-inverse matrix and the target satellite signal to obtain a target column vector;

[0230] a solving unit, configured to solve the target column vector to obtain a phase matrix of the target satellite signal.

[0231] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0232] In the several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0233] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0234] In addition, each functional unit in various embodiments of the present application can be integrated into one processing unit, or each functional unit can be a separate physical unit, or two or more functional units can be integrated into one processing unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0235] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0236] The terms "first", "second", "third", "fourth" and the like used in the specification of the present application and the above-described drawings, if any, are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented, for example, in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0237] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for acquiring a Beidou satellite signal, characterized in that, The method comprises: acquiring a target satellite signal; calculating a covariance matrix of the target satellite signal; acquiring a target hyperparameter corresponding to the target satellite signal; constructing a propagator according to the covariance matrix and the target hyperparameter; calculating a frequency matrix and a phase matrix of the target satellite signal according to the propagator; the acquiring of the target hyperparameter corresponding to the target satellite signal comprises: extracting a second target signal feature from the target satellite signal; inputting the second target signal feature into a pre-constructed regression model to output a target hyperparameter; the propagator is: ； for the propagation sub-matrix, for the identity matrix, R is the covariance matrix, for the target hyperparameters, H denotes the Hermitian transpose; the calculating of the frequency matrix and the phase matrix of the target satellite signal according to the propagator comprises: constructing a spatial spectrum function according to the propagator; traversing the spatial spectrum function to determine a spectrum peak of the spatial spectrum function, and determining a frequency matrix of the target satellite signal according to the spectrum peak; calculating a pseudo-inverse matrix of the frequency matrix according to the frequency matrix of the target satellite signal; performing least squares on the pseudo-inverse matrix and the target satellite signal to obtain a target column vector; solving the target column vector to obtain a phase matrix of the target satellite signal; the calculating of the covariance matrix of the target satellite signal comprises: performing frequency sampling on the target satellite signal to obtain a blind separation model corresponding to the target satellite signal; performing autocorrelation matrix calculation on the blind separation model to obtain the covariance matrix of the target satellite signal.

2. The method of claim 1, wherein, The acquiring of the target satellite signal comprises: acquiring an initial Beidou satellite signal; extracting a first target signal feature from the initial Beidou satellite signal; inputting the first target signal feature into a pre-trained denoising model to output the target satellite signal.

3. The method of claim 2, wherein, The training steps of the denoising model comprise: acquiring original data and noise-free signal data; the original data is signal data fused with a Beidou satellite signal and background noise; performing data preprocessing on the original data to obtain an original data sequence, and extracting an original signal feature from the original data sequence; constructing a recurrent neural network model; training the recurrent neural network model by taking the original signal feature as the input of the recurrent neural network model and taking the noise-free signal data as the output of the recurrent neural network model, to obtain the denoising model.

4. The method of claim 1, wherein, The training steps of the regression model comprise: acquiring an experimental data set, the experimental data set comprising sample signal features of noise-free signal data and hyperparameter experimental values; constructing an initial regression model, taking the sample signal features as the input of the initial regression model, taking the hyperparameter experimental values as the output of the initial regression model, and training the initial regression model by using a support vector regression method to obtain optimal model parameters of the initial regression model; constructing the regression model according to the optimal model parameters and the initial regression model.

5. The method of claim 1, wherein, The blind separation model is: ； ； ； ； ； ； ； ； ； ； ； ； ； wherein, denotes complex space, T denotes transpose, H denotes Hermitian transpose; x represents the same sampling point of different channel acquisition of Beidou satellite signal, M represents the number of sampling channels; t g denotes the number of samples; A k denotes the amplitude of the received kth Beidou satellite signal; denotes the initial phase of the received kth Beidou satellite signal; P k denotes the product of the received kth Beidou satellite signal code and navigation code; F k is the frequency of the kth Beidou satellite signal; is the intermediate frequency carrier frequency, and respectively denote the frequency and initial phase of the kth Beidou satellite signal collected by the receiver, is the background noise at time t; the calculation formula of the covariance matrix of the target satellite signal is: ； where R xx is the covariance matrix of the satellite signal sent by the Beidou satellite, I is the unit matrix, E is the mathematical expectation, and R is the covariance matrix of the target satellite signal received by the receiver.

6. A Beidou satellite signal acquisition device, characterized in that, The device comprises a first acquiring module configured to acquire a target satellite signal; The first calculation module is configured to calculate a covariance matrix of the target satellite signal. The second acquisition module is configured to acquire a target hyperparameter corresponding to the target satellite signal. The construction module is configured to construct a propagator according to the covariance matrix and the target hyperparameter. The second calculation module is configured to calculate a frequency matrix and a phase matrix of the target satellite signal according to the propagator. The second acquisition module includes: The second extraction unit is configured to extract a second target signal feature from the target satellite signal. The target hyperparameter output unit is configured to input the second target signal feature into a pre-constructed regression model and output a target hyperparameter. The propagator is ； for the propagation sub-matrix, for the identity matrix, R is the covariance matrix, for the target hyperparameters, H denotes the Hermitian transpose; The second calculation module includes: The spatial spectrum function construction unit is configured to construct a spatial spectrum function according to the propagator. The frequency matrix determination unit is configured to traverse the spatial spectrum function, determine a spectral peak of the spatial spectrum function, and determine a frequency matrix of the target satellite signal according to the spectral peak. The pseudo-inverse matrix calculation unit is configured to calculate a pseudo-inverse matrix of the frequency matrix of the target satellite signal according to the frequency matrix. The operation unit is configured to perform least squares on the pseudo-inverse matrix and the target satellite signal to obtain a target column vector. The solving unit is configured to solve the target column vector to obtain a phase matrix of the target satellite signal. The first calculation module includes: The model conversion unit is configured to perform frequency sampling on the target satellite signal to obtain a blind source separation model corresponding to the target satellite signal. The first calculation unit is configured to perform autocorrelation matrix calculation on the blind source separation model to obtain the covariance matrix of the target satellite signal.

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