A method and apparatus for acquiring medium- and long-code satellite navigation signals

Abstract

This invention discloses a method and apparatus for acquiring medium- and long-code satellite navigation signals. The invention includes acquiring incident intermediate frequency (IF) signals from multiple down-converted satellites and constructing a satellite real matrix based on these signals; performing eigenvalue decomposition on the satellite real matrix using orthogonal trigonometric decomposition to determine the signal noise subspace; determining a Doppler frequency shift estimate based on the signal noise subspace and a preset signal frequency vector; determining a pseudocode phase offset using the extended replication overlap method, the Doppler frequency shift estimate, and a preset local pseudocode; and generating a target satellite navigation IF signal using the pseudocode phase offset and the Doppler frequency shift estimate. This invention solves the technical problem of excessively long acquisition time in existing medium- and long-code satellite navigation signal acquisition technologies.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a method and apparatus for acquiring medium- and long-code satellite navigation signals. Background Technology

[0002] With the development of aerospace technology and the deepening of space exploration, the Global Navigation Satellite System (GNSS) can provide precise position and time information for any location on Earth and in near-Earth space, 24 / 7. Maintaining high accuracy in satellite orbit determination and clock error determination is crucial for ensuring that GNSS receivers achieve the positioning or timing accuracy required by the large system.

[0003] GNSS receivers acquire sufficient visible satellite signals and replicate and track these signals, enabling users to obtain the positioning, velocity, and timing information they are interested in. Therefore, acquisition technology is a crucial part of GNSS receiver signal processing, and the quality of its acquisition method is one of the main criteria for evaluating a receiver. Reliably acquiring satellite navigation signals and improving the receiver's acquisition sensitivity are key to improving navigation and positioning performance. Structurally, satellite navigation signals can be divided into three layers: carrier, pseudocode, and data code.

[0004] Most existing medium- and long code satellite navigation signal acquisition technologies employ Fast Fourier Transform (FFT) to convert correlation operations in the time domain into multiplication operations in the frequency domain, thereby calculating the correlation value and determining whether it exceeds the acquisition threshold. If it does, the acquisition is successful; otherwise, the carrier frequency is adjusted for re-acquisition. However, due to the long period of medium- and long codes, the number of FFT operations required increases, resulting in excessively long acquisition times. Summary of the Invention

[0005] This invention provides a method and apparatus for acquiring medium- and long-code satellite navigation signals, which solves the technical problem of excessively long acquisition time in existing medium- and long-code satellite navigation signal acquisition technologies.

[0006] The first aspect of this invention provides a method for acquiring medium- and long-code satellite navigation signals, comprising:

[0007] Acquire incident intermediate frequency (IF) signals from multiple downconverted satellites, and construct a satellite real number matrix based on the incident IF signals from each downconverted satellite.

[0008] The satellite real matrix is ​​eigenvalued by orthogonal trigonometric decomposition to determine the signal-noise subspace.

[0009] Based on the signal-noise subspace and the preset signal frequency vector, the Doppler frequency shift estimate is determined;

[0010] Based on the extended replication overlap method, the Doppler frequency shift estimate and the preset local pseudocode are used to determine the pseudocode phase shift.

[0011] The target satellite navigation intermediate frequency signal is generated using the pseudocode phase offset and the Doppler frequency shift estimate.

[0012] Optionally, the step of constructing a satellite real matrix based on the incident intermediate frequency signals of each down-converted satellite includes:

[0013] The incident intermediate frequency signals of each down-converted satellite are sampled by a preset uniform linear array, and multiple down-converted output signals are output. A received signal matrix is ​​constructed using each down-converted output signal.

[0014] In the received signal matrix, the matrix column data of the preset array sequence number position are selected for mean processing to determine multiple mean signal data;

[0015] Using the mean signal data, a Toplitz matrix is ​​constructed, and a conjugate smoothing operation is performed on the Toplitz matrix to generate a satellite complex matrix;

[0016] Perform matrix transformation on the satellite complex matrix to determine the satellite real matrix.

[0017] Optionally, the step of determining the Doppler frequency shift estimate based on the signal-noise subspace and the preset signal frequency vector includes:

[0018] A frequency polynomial is constructed using a preset signal frequency vector and the noise subspace in the signal noise subspace.

[0019] Solve the frequency polynomial to determine the intermediate frequency signal frequency;

[0020] The Doppler frequency shift estimate is determined by performing a difference calculation between the intermediate frequency signal frequency and the preset carrier frequency.

[0021] Optionally, the step of determining the pseudocode phase shift based on the extended replication overlap method, using the Doppler frequency shift estimate and a preset local pseudocode, includes:

[0022] Carrier stripping is performed on the Doppler frequency shift estimate to generate a pseudo-code signal;

[0023] Based on the data length of the pseudocode signal, the preset local pseudocode is truncated to generate multiple local pseudocode segments.

[0024] The local pseudocode segments are selected sequentially according to the preset addition quantity and added to determine multiple new local pseudocode sequences;

[0025] Perform a Fast Fourier Transform on the pseudo-code signal to determine the frequency domain signal corresponding to the pseudo-code signal;

[0026] Perform a Fast Fourier Transform on each of the new local pseudocode sequences and take their conjugates to generate the local pseudocode transform sequence corresponding to each of the new local pseudocode sequences.

[0027] Perform fast Fourier transform multiplication and fast Fourier transform inverse operations on each of the local pseudocode transform sequences and the frequency domain signal respectively, and output the first pseudocode phase correlation value corresponding to each of the new local pseudocode sequences;

[0028] The local pseudocode sequence corresponding to the largest first pseudocode phase correlation value is taken as the target local pseudocode sequence.

[0029] The local pseudocode segments of the target local pseudocode new sequence with a preset sum of local pseudocode segments are correlated with the pseudocode signal, and the correlation value of the pseudocode phase and the second pseudocode phase corresponding to the preset sum of local pseudocode segments is output.

[0030] The pseudocode phase corresponding to the largest second pseudocode phase correlation value is taken as the pseudocode phase offset.

[0031] Optionally, the frequency polynomial is specifically:

[0032]

[0033] in, a(z) = [1, z, ..., z M ] T ;

[0034] f(z) is the frequency polynomial; z is an element in the frequency search vector, j is the imaginary unit, ω is the digital frequency; M is the index of the sensor's position in the uniform linear antenna array; U is the unitary matrix; Q n Let K be the noise subspace, with dimensions (M+1)×((M+1)-K), where K is the number of incident intermediate frequency signals from down-converted satellites containing different frequency components; (·) T This is for the transpose operation; (·) H For conjugate transpose operation; a(z) is the frequency search vector; I is the identity matrix with a dimension of (M+1)×(M+1); J is the subdiagonal matrix with subdiagonal elements of 1 and other elements of 0, and its dimension is (M+1)×(M+1).

[0035] Optionally, the calculation process of the satellite real matrix is ​​as follows:

[0036]

[0037] in, Let `real` be the real matrix of the satellite; `real(·)` is the real part function. Let J be the satellite complex matrix; imag(·) is the imaginary part function; J is the subdiagonal matrix, where the subdiagonal elements are 1 and the other elements are 0, and its dimension is (M+1)×(M+1).

[0038] A second aspect of the present invention provides a medium-to-long code satellite navigation signal acquisition device, comprising:

[0039] The acquisition module is used to acquire incident intermediate frequency signals from multiple down-conversion satellites and construct a satellite real number matrix based on the incident intermediate frequency signals from each down-conversion satellite.

[0040] A module is used to perform eigenvalue decomposition on the satellite real matrix using the orthogonal triangular decomposition method to determine the signal-noise subspace;

[0041] The module is used to determine the Doppler frequency shift estimate based on the signal-noise subspace and the preset signal frequency vector.

[0042] The extension module is used to determine the pseudocode phase offset based on the extended replication overlap method, using the Doppler frequency shift estimate and the preset local pseudocode;

[0043] The generation module is used to generate the target satellite navigation intermediate frequency signal using the pseudocode phase offset and the Doppler frequency shift estimate.

[0044] A computer device provided in a third aspect of the present invention includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the medium- and long-code satellite navigation signal acquisition method as described in any of the preceding claims.

[0045] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, wherein the computer program / instructions, when executed by a processor, implement the steps of the medium-length code satellite navigation signal acquisition method as described in any of the preceding claims.

[0046] The fifth aspect of the present invention provides a computer program product comprising a computer program / instructions, wherein when the computer program / instructions are executed by a processor, the steps of the medium- and long-code satellite navigation signal acquisition method as described in any of the preceding claims are implemented.

[0047] As can be seen from the above technical solutions, the present invention has the following advantages:

[0048] The above-mentioned technical solution of the present invention provides a method for acquiring medium- and long-code satellite navigation signals. This method acquires incident intermediate frequency (IF) signals from multiple down-converted satellites and constructs a satellite real matrix based on these signals. It then uses an orthogonal triangular decomposition method to perform eigenvalue decomposition on the satellite real matrix to determine the signal noise subspace. Based on the signal noise subspace and a preset signal frequency vector, it determines a Doppler frequency shift estimate. Using an extended replication overlap method, it determines a pseudocode phase shift based on the Doppler frequency shift estimate and a preset local pseudocode. Finally, it generates a target satellite navigation IF signal using the pseudocode phase shift and the Doppler frequency shift estimate. Based on this method, the orthogonal triangular decomposition method and the extended replication overlap method are used to generate the target satellite navigation IF signal from the acquired incident IF signals from multiple down-converted satellites. This process uses the orthogonal triangular decomposition method and the extended replication overlap method to estimate the frequency in one dimension and the pseudocode phase in one dimension, which reduces the complexity of the two-dimensional search and further reduces the acquisition time. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating the steps of a medium- and long-code satellite navigation signal acquisition method provided in this embodiment of the invention;

[0051] Figure 2 A flowchart illustrating the steps of another medium- and long-code satellite navigation signal acquisition method provided in this embodiment of the invention;

[0052] Figure 3 A flowchart illustrating the medium- and long-code satellite navigation signal acquisition method provided in this embodiment of the invention;

[0053] Figure 4 This is a structural block diagram of a medium-length code satellite navigation signal acquisition device provided in an embodiment of the present invention. Detailed Implementation

[0054] This invention provides a method and apparatus for acquiring medium- and long-code satellite navigation signals, which solves the technical problem of excessively long acquisition time in existing medium- and long-code satellite navigation signal acquisition technologies.

[0055] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0056] Please see Figure 1 , Figure 1 A flowchart illustrating the steps of a long-code satellite navigation signal acquisition method provided in an embodiment of the present invention.

[0057] This invention provides a method for acquiring medium- and long-code satellite navigation signals, comprising:

[0058] Step 101: Obtain the incident intermediate frequency signals of multiple downconversion satellites, and construct a satellite real number matrix based on the incident intermediate frequency signals of each downconversion satellite.

[0059] The incident intermediate frequency (IF) signals from multiple downconversion satellites are multiple downconversion satellite incident IF signals with different frequency components.

[0060] It should be noted that after acquiring the incident intermediate frequency (IF) signals from multiple down-converted satellites, a preset uniform linear array is used to perform T reception samplings on these signals, thereby obtaining multiple down-converted output signals from the preset uniform linear array. In other words, the preset uniform linear array samples the acquired incident IF signals from multiple down-converted satellites and outputs multiple down-converted output signals. The preset uniform linear array is a uniform linear antenna array composed of 2M+1 sensors. The incident IF signals from the down-converted satellites can be represented as:

[0061]

[0062] Wherein, S is the incident intermediate frequency signal matrix composed of all incident intermediate frequency signals from down-converted satellites, and the dimension of the incident intermediate frequency signal matrix is ​​K×T; s1(1) is the incident intermediate frequency signal of the down-converted satellite of the first frequency component under the first sampling; s1(2) is the incident intermediate frequency signal of the down-converted satellite of the first frequency component under the second sampling; s1(T) is the incident intermediate frequency signal of the down-converted satellite of the first frequency component under the Tth sampling; s2(1) is the incident intermediate frequency signal of the down-converted satellite of the second frequency component under the first sampling; s2(2) is the incident intermediate frequency signal of the down-converted satellite of the second frequency component under the second sampling; s2(T) is the incident intermediate frequency signal of the down-converted satellite of the second frequency component under the Tth sampling; s K (1) represents the down-converted satellite incident intermediate frequency signal of the Kth frequency component in the first sampling; sK (2) is the down-converted satellite incident intermediate frequency signal of the Kth frequency component in the second sampling; s K (T) represents the incident intermediate frequency signal of the down-converted satellite with the Kth frequency component under the Tth sampling; K is the number of incident intermediate frequency signals of the down-converted satellite containing different frequency components; T is the number of sampling times.

[0063] Furthermore, a received signal matrix is ​​constructed using the output signals of each down-conversion frequency converter. The specific process for constructing the received signal matrix is ​​as follows:

[0064] X = AS + N;

[0065]

[0066]

[0067] Where X is the received signal matrix with a dimension of (2M+1)×T; A is the array flow matrix with a dimension of (2M+1)×K; N is the noise matrix with a dimension of (2M+1)×T; S is the incident intermediate frequency signal matrix composed of the incident intermediate frequency signals from all downconverted satellites with a dimension of K×T; f K f1 is the frequency of the intermediate frequency (IF) signal incident on the Kth downconversion satellite; K is the number of IF signals incident on the downconversion satellite containing different frequency components; f2 is the frequency of the IF signal incident on the second downconversion satellite; T is the number of samples; f1 is the frequency of the IF signal incident on the first downconversion satellite; f2 is the frequency of the IF signal incident on the second downconversion satellite; f3 is the frequency of the IF signal incident on the third downconversion satellite; f4 is the frequency of the IF signal incident on the fourth downconversion satellite; f5 is the frequency of the IF signal incident on the fifth downconversion satellite; f6 is the frequency of the IF signal incident on the sixth downconversion satellite; f7 is the frequency of the IF signal incident on the seventh downconversion satellite; f8 is the frequency of the IF signal incident on the eighth downconversion satellite; f9 is the frequency of the IF signal incident on the ninth downconversion satellite; f1 is the frequency of the IF signal incident on the first down s The sampling frequency is M; M is the index of the sensor's position in the uniform linear antenna array; x -M+1 (1) is the down-conversion output signal of the sensor located at -M+1 during the first sampling; x -M+1 (2) is the down-conversion output signal of the sensor located at -M+1 during the second sampling; x -M+1 (T) represents the down-conversion output signal of the sensor located at -M+1 during the Tth sampling; x -M (1) is the down-conversion output signal of the sensor located at -M during the first sampling; x -M (2) is the down-conversion output signal of the sensor located at -M during the second sampling; x -M (T) represents the down-conversion output signal of the sensor located at -M during the Tth sampling; x M (1) is the down-conversion output signal of the sensor located at M during the first sampling; x M (2) is the down-conversion output signal of the sensor located at M during the second sampling; x M(T) is the down-conversion output signal of the sensor located at M under the Tth sampling; j is the imaginary unit.

[0068] Further, L column vectors are selected in the received signal matrix and averaged to obtain averaged data. Specifically, the matrix column data at the first preset array index position in the received signal matrix is ​​averaged to determine multiple average signal data. For example, if the preset array index position is set to 10, and the total number of columns in the received signal matrix is ​​20, then the first 10 columns of matrix column data (vectors) in the received signal matrix are averaged, and the average signal data for each of these 10 columns is output. The preset array index position can be set as needed, and this invention is not limited to this. The processing procedure for the average signal data is as follows:

[0069]

[0070] Where z is the mean signal matrix composed of all mean signal data; L is the preset array index; X(i) is the matrix column data of the i-th column; z -M z0 is the mean signal data corresponding to the sensor located at -M; z0 is the mean signal data corresponding to the sensor located at 0 ... M This represents the mean signal data corresponding to the sensor located at point M; (·) T This is for the transpose operation.

[0071] Furthermore, the Toplitz matrix is ​​constructed using the mean-normalized data. Specifically, the Toplitz matrix is ​​constructed using the mean signal data, and a conjugate smoothing operation is performed on the Toplitz matrix to generate the satellite complex matrix. Matrix transformation is then performed on the satellite complex matrix to determine the satellite real matrix. The Toplitz matrix can be represented as:

[0072]

[0073] Where Z is the Topplitz matrix; z0 is the mean signal data corresponding to the sensor located at 0; z -1 This represents the mean signal data corresponding to the sensor located at -1; z -M z1 represents the mean signal data corresponding to the sensor located at -M; z2 represents the mean signal data corresponding to the sensor located at 1; z3 represents the mean signal data corresponding to the sensor located at -M. -M+1 z1 is the mean signal data corresponding to the sensor located at -M+1; z2 is the mean signal data corresponding to the sensor located at 2; z -M+2 This represents the mean signal data corresponding to the sensor located at -M+2; z M The mean signal data corresponding to the sensor located at point M; z M-1 This represents the mean signal data corresponding to the sensor located at M-1.

[0074] Furthermore, the processing procedure for the satellite complex matrix is ​​as follows:

[0075]

[0076] in, Z is the complex matrix of the satellite; Z is the Topplitz matrix; Z H Z is the conjugate transpose of the Topulitz matrix; J is a subdiagonal matrix with subdiagonal elements of 1 and other elements of 0, and its dimension is (M+1)×(M+1); Z * It is the conjugate of the Toplitz matrix.

[0077] Furthermore, the calculation process for the satellite real matrix is ​​as follows:

[0078]

[0079] in, Let `real` be the real matrix of the satellite; `real(·)` is the real part function. Let J be the satellite complex matrix; imag(·) is the imaginary part function; J is the subdiagonal matrix, where the subdiagonal elements are 1 and the other elements are 0, and its dimension is (M+1)×(M+1).

[0080] For example, the down-converted satellite incident intermediate frequency signal obtained by the present invention can be taken as a GPS L2C signal:

[0081]

[0082] in, P is the intermediate frequency signal after down-conversion of the radio frequency signal of the i-th satellite acquired at time t; (i) denoted as , where is the power of the intermediate frequency signal after down-conversion of the RF signal from the i-th satellite; D is the navigation message; τ is the propagation delay; C CM For random noise CM code; C CL f is the random noise CL code; f1 is the intermediate frequency (IF) of the down-converted IF signal from the satellite radio frequency signal; f d The Doppler frequency offset of the intermediate frequency signal after down-conversion of the satellite radio frequency signal; The carrier phase is n(t); the mean σ is n(t). 2 The noise is Gaussian white noise with zero variance; t is time t.

[0083] In this embodiment, incident intermediate frequency (IF) signals from multiple downconversion satellites are acquired, and a satellite real number matrix is ​​constructed based on the incident IF signals from each downconversion satellite.

[0084] Step 102: Use the orthogonal triangular decomposition method to perform eigenvalue decomposition on the satellite real matrix to determine the signal-noise subspace.

[0085] The signal-noise subspace includes the signal subspace and the noise subspace.

[0086] The orthogonal triangular decomposition method is called QR decomposition.

[0087] It should be noted that, based on the satellite real matrix obtained above, QR eigenvalue decomposition is performed to obtain the corresponding signal-noise subspace, namely the signal subspace and the noise subspace; the processing procedure for the signal-noise subspace is as follows:

[0088]

[0089] Where QR(·) is the QR decomposition function; Q s Let Q be the signal subspace, and let Q be the signal subspace with dimension (M+1)×K. n Let R be the noise subspace, and let R be the dimension of the noise subspace (M+1)×((M+1)-K). s O is the upper triangular signal space matrix with a dimension of K×(M+1); O is the lower triangular matrix with all elements equal to 0 and a dimension of ((M+1)-K)×(M+1), where M is the index of the sensor position in the uniform linear antenna array; and K is the number of down-converted satellite incident intermediate frequency signals containing different frequency components.

[0090] In this embodiment, the orthogonal triangular decomposition method is used to perform eigenvalue decomposition on the satellite real matrix to determine the signal-noise subspace.

[0091] Step 103: Determine the Doppler frequency shift estimate based on the signal noise subspace and the preset signal frequency vector.

[0092] It should be noted that a frequency polynomial is constructed using a preset signal frequency vector and a noise subspace within the signal-noise subspace. Specifically, a frequency polynomial with frequency as the unknown is constructed based on the orthogonality between the preset signal frequency vector and the noise subspace. The frequency polynomial is as follows:

[0093]

[0094] in, a(z) = [1, z, ..., z M ] T ;

[0095] f(z) is the frequency polynomial; z is an element in the frequency search vector, j is the imaginary unit, ω is the digital frequency; M is the index of the sensor's position in the uniform linear antenna array; U is the unitary matrix; Q nLet K be the noise subspace, with dimensions (M+1)×((M+1)-K), where K is the number of incident intermediate frequency signals from down-converted satellites containing different frequency components; (·) T This is for the transpose operation; (·) H For conjugate transpose operation; a(z) is the frequency search vector; I is the identity matrix with a dimension of (M+1)×(M+1); J is the subdiagonal matrix with subdiagonal elements of 1 and other elements of 0, and its dimension is (M+1)×(M+1).

[0096] Further, the roots of the frequency polynomial are solved to obtain the intermediate frequency (IF) signal frequency. That is, the IF signal frequency is determined by solving the frequency polynomial. Then, based on the obtained frequency estimate, that is, the difference between the IF signal frequency and the preset carrier frequency, the estimated value of the Doppler frequency shift is obtained. That is, the difference between the IF signal frequency and the preset carrier frequency is calculated to determine the estimated value of the Doppler frequency shift. Here, the IF signal frequency includes the carrier frequency and the Doppler frequency shift; the preset carrier frequency is the carrier frequency known by the local carrier generator.

[0097] In this embodiment, the Doppler frequency shift estimate is determined based on the signal-noise subspace and a preset signal frequency vector.

[0098] Step 104: Based on the extended replication overlap method, the phase offset of the pseudocode is determined using the Doppler frequency shift estimate and the preset local pseudocode.

[0099] It should be noted that, firstly, carrier stripping is performed on the Doppler frequency shift estimate to generate a pseudocode signal. After carrier stripping, signal acquisition is switched from a two-dimensional search to a one-dimensional search of the pseudocode phase. Next, the preset local pseudocode is truncated according to the data length of the pseudocode signal to generate multiple local pseudocode segments. For example, if the data length T of the acquired pseudocode signal is 1000, and the data length of the preset local pseudocode is 10000, then the preset local pseudocode can be truncated into 10 local pseudocode segments of length 1000, that is, multiple local pseudocode segments with the same data length T as the pseudocode signal, such as c1=[c(1),c( 2),…,c(T)],c2=[c(T+1),c(T+2),…,c(2T)], where c1 is the first local pseudocode segment, c2 is the second local pseudocode segment, c(1) is the local pseudocode signal corresponding to the first sampling; c(2) is the local pseudocode signal corresponding to the second sampling; c(T) is the local pseudocode signal corresponding to the Tth sampling; c(T+1) is the local pseudocode signal corresponding to the T+1th sampling; c(T+2) is the local pseudocode signal corresponding to the T+2th sampling; c(2T) is the local pseudocode signal corresponding to the 2Tth sampling; it can be divided into T c / T local pseudocode segments, T cThe data length of the preset local pseudocode obtained at the same sampling frequency.

[0100] Furthermore, local pseudocode segments are selected sequentially according to a preset addition quantity for addition operations to determine multiple new local pseudocode sequences. For example, if the preset addition quantity is 2 and the number of local pseudocode segments is 6, then every two pseudocode segments are added sequentially to form a new pseudocode sequence. That is, the first and second local pseudocode segments are added, the third and fourth local pseudocode segments are added, and the fifth and sixth local pseudocode segments are added, thus obtaining three new local pseudocode sequences. For example, the new local pseudocode sequence can be represented as:

[0101] c1'=[c(1)+c(T+1),c(2)+c(T+2),…,c(T)+c(2T)];

[0102] c'2=[c(2T+1)+c(3T+1),c(2T+2)+c(3T+2),…,c(3T)+c(4T)];

[0103] Wherein, c1′ is the first new local pseudocode sequence after superposition; c2′ is the second new local pseudocode sequence after superposition; c(1) is the local pseudocode signal corresponding to the first sampling; c(2) is the local pseudocode signal corresponding to the second sampling; c(T) is the local pseudocode signal corresponding to the Tth sampling; c(T+1) is the local pseudocode signal corresponding to the T+1th sampling; c(2T) is the local pseudocode signal corresponding to the 2Tth sampling; c(2T+1) is the local pseudocode signal corresponding to the 2T+1th sampling. The local pseudocode signal corresponding to the first sample; c(T+2) is the local pseudocode signal corresponding to the (T+2)th sample; c(3T+1) is the local pseudocode signal corresponding to the (3T+1)th sample; c(2T+2) is the local pseudocode signal corresponding to the (2T+2)th sample; c(3T+2) is the local pseudocode signal corresponding to the (3T+2)th sample; c(3T) is the local pseudocode signal corresponding to the (3T)th sample; c(4T) is the local pseudocode signal corresponding to the (4T)th sample.

[0104] In this embodiment, the preset addition quantity can be selected as needed to add the number of segments. The present invention is not limited to this, but considering that adding multiple signal segments (local pseudocode segments) will lead to noise accumulation, the number of signal segments added should not be too many.

[0105] Furthermore, after performing a Fast Fourier Transform (FFT) on each new local pseudocode sequence and taking its conjugate, a Fast Fourier Transform is performed on the pseudocode sequence, which is then multiplied by the conjugate FFT of the new local pseudocode sequence, followed by an inverse FFT to obtain the correlation value at each pseudocode phase. Specifically, an FFT is performed on the pseudocode signal to determine the corresponding frequency domain signal; a FFT is performed on each new local pseudocode sequence and its conjugate is taken to generate the corresponding local pseudocode transform sequence; a FFT multiplication and an inverse FFT operation are performed on each local pseudocode transform sequence and the frequency domain signal, respectively. That is, each local pseudocode transform sequence is multiplied by the frequency domain signal using an FFT, and then the inverse FFT operation is performed to output the correlation value at the pseudocode phase corresponding to each new local pseudocode sequence, i.e., the first pseudocode phase correlation value. Each first pseudocode phase correlation value corresponds to a first pseudocode phase.

[0106] Furthermore, among all the first pseudocode phase correlation values, the correlation peak is found, that is, the local pseudocode new sequence corresponding to the largest first pseudocode phase correlation value is found as the target local pseudocode new sequence. If the preset addition quantity is 2, the pseudocode phase corresponding to the largest first pseudocode phase correlation value is composed of the superposition of two local pseudocode segments. Therefore, it is necessary to further determine in which local pseudocode segment the correlation peak (the largest first pseudocode phase correlation value) is obtained. Let the two local pseudocode segments perform correlation operations with the pseudocode signal respectively to obtain the pseudocode phase and the second pseudocode phase correlation value corresponding to the two local pseudocode segments. The pseudocode phase corresponding to the largest second pseudocode phase correlation value is used as the pseudocode phase offset.

[0107] For example, assuming the preset local pseudocode data length is 10000 and the pseudocode signal data length is 1000, the preset local pseudocode can be divided into 10 local pseudocode segments of length 1000. The preset addition quantity is set to 2, and each pair of local pseudocode segments is added sequentially, resulting in 5 new local pseudocode sequences. These 5 new local pseudocode sequences are then correlated with the pseudocode signal. If a peak is found in the second new local pseudocode sequence, this peak may be due to a high correlation between the third or fourth local pseudocode segment and the pseudocode signal. Therefore, it is necessary to further determine which local pseudocode segment it is. Thus, these two local pseudocode segments are correlated with the pseudocode signal respectively. If a peak is found in the third local pseudocode segment, the correlated phase is the pseudocode phase shift.

[0108] In this embodiment, based on the extended replication overlap method, the Doppler frequency shift estimate and the preset local pseudocode are used to determine the pseudocode phase shift.

[0109] Step 105: Generate satellite navigation intermediate frequency signal using pseudocode phase offset and Doppler frequency shift estimates.

[0110] It should be noted that after completing the above steps, the pseudo-code phase offset and Doppler frequency shift estimates of the satellite navigation signal can be obtained. Based on the pseudo-code phase offset and Doppler frequency shift estimates, the satellite navigation intermediate frequency signal is generated, completing the signal acquisition process and providing a guarantee for the subsequent signal tracking loop. This method can solve the technical problems of large Doppler frequency shift estimation errors and high complexity of two-dimensional plane search in low signal-to-noise ratio or dynamic scenarios. The medium-length code satellite navigation signal acquisition method proposed in this invention can be applied to aerospace, navigation and positioning, signal acquisition and other fields. The process of generating the satellite navigation intermediate frequency signal based on the pseudo-code phase offset and Doppler frequency shift estimates can refer to the processing process of existing technologies, and this invention is not limited thereto.

[0111] In this embodiment, pseudocode phase offset and Doppler frequency shift estimates are used to generate satellite navigation intermediate frequency signals.

[0112] In this embodiment of the invention, a method for acquiring medium- and long-code satellite navigation signals is provided. The method involves acquiring the incident intermediate frequency (IF) signal from a down-converted satellite and constructing a satellite real matrix based on the IF signal. An orthogonal triangular decomposition method is used to perform eigenvalue decomposition on the satellite real matrix to determine the signal noise subspace. Based on the signal noise subspace and a preset signal frequency vector, a Doppler frequency shift estimate is determined. Using an extended replication overlap method, the Doppler frequency shift estimate and a preset local pseudocode are used to determine the pseudocode phase shift. The pseudocode phase shift and the Doppler frequency shift estimate are used to generate the satellite navigation IF signal. Based on the above scheme, the orthogonal triangular decomposition method and the extended replication overlap method are used to generate the satellite navigation IF signal from the acquired down-converted satellite incident IF signal. This process uses the orthogonal triangular decomposition method and the extended replication overlap method to estimate the frequency in one dimension and the pseudocode phase in one dimension, which reduces the complexity of the two-dimensional search and further reduces the acquisition time.

[0113] Please see Figure 2 , Figure 2 A flowchart illustrating the steps of another medium- and long-code satellite navigation signal acquisition method provided in this embodiment of the invention.

[0114] This invention provides a method for acquiring medium- and long-code satellite navigation signals, comprising:

[0115] Step 201: Obtain the incident intermediate frequency signals of multiple downconversion satellites, and construct a satellite real number matrix based on the incident intermediate frequency signals of each downconversion satellite.

[0116] The incident intermediate frequency (IF) signals from multiple downconversion satellites are multiple downconversion satellite incident IF signals with different frequency components.

[0117] For example, the intermediate frequency signal incident from a down-converted satellite can be taken as an example of the GPS satellite navigation signal L2C signal. The L2C code adopts a completely new signal structure, which is composed of a civilian medium-length CM code modulated with navigation messages and a civilian long code CL code used as pilot codes, using time-division multiplexing. The expression of the GPS L2C signal is as follows:

[0118]

[0119] Among them, S L2C (t) represents the GPS L2C signal acquired at time t; P represents the power of the GPS L2C signal; D represents the navigation message; τ represents the propagation delay; C CM For random noise CM code; C CL f is the random noise CL code; f1 is the intermediate frequency of the GPS L2C signal; f d This refers to the Doppler frequency offset of the GPS L2C signal. The carrier phase is n(t); the mean σ is n(t). 2 The noise is Gaussian white noise with zero variance; t is time t.

[0120] Further, step 201 may include the following sub-steps:

[0121] S11. The intermediate frequency signals incident on each down-conversion satellite are sampled by a preset uniform linear array, and multiple down-conversion output signals are output. The received signal matrix is ​​constructed using each down-conversion output signal.

[0122] S12. Select the matrix column data of the preset array sequence number position in the received signal matrix and perform mean processing to determine multiple mean signal data.

[0123] S13. Using the mean signal data, construct the Toplitz matrix and perform conjugate smoothing operation on the Toplitz matrix to generate the satellite complex matrix;

[0124] S14. Perform matrix transformation on the satellite complex matrix to determine the satellite real matrix.

[0125] For example, suppose there is a 2M+1 uniform linear receiving antenna array (preset uniform linear array), the element spacing is half a wavelength, and the data received at sampling time t, that is, the down-converted output signal of the uniform linear receiving antenna array, is:

[0126]

[0127] Where X(t) is the received signal matrix composed of multiple down-conversion output signals sampled at time t; x -Mx(t) represents the down-conversion output signal of the sensor located at -M at time t; x0(t) represents the down-conversion output signal of the sensor located at 0 at time t; x M (t) represents the down-conversion output signal of the sensor located at M at time t; A(ω) is the array manifold matrix with dimension (2M+1)×1, A(ω)=[a(ω1),a(ω2),…,a(ω) K )], a(ω1) is the frequency vector of the intermediate frequency signal incident from the first down-conversion satellite, and a(ω2) is the frequency vector of the intermediate frequency signal incident from the second down-conversion satellite. K Let u be the frequency vector of the intermediate frequency signal incident on the Kth downconversion satellite. K The phase difference between the output signals of adjacent sensors. It is the conjugate of the phase difference between the output signals of adjacent sensors. ω represents the phase difference between the output signals of the sensor located at M and the sensor located at 0. K Let f be the digital frequency of the intermediate frequency signal incident on the Kth downconversion satellite. K Let f be the frequency of the intermediate frequency signal incident on the Kth downconversion satellite, j be the imaginary unit, and f be the frequency of the intermediate frequency signal incident on the Kth downconversion satellite. s S is the sampling frequency; S(t) is the incident intermediate frequency signal matrix composed of multiple noise-free down-conversion satellite incident intermediate frequency signals acquired at time t, S(t)=[s1(t),s2(t),…,s K (t)] T s1(t) is the first frequency component of the down-converted satellite incident intermediate frequency signal at time t, and s2(t) is the second frequency component of the down-converted satellite incident intermediate frequency signal at time t. K (t) represents the incident intermediate frequency signal of the down-converted satellite at time t, which is the Kth frequency component; n(t) is the noise matrix at time t, n(t) = [n -M (t),…,n0(t),…,n M (t)] T n -M n(t) represents the noise sample data of the sensor located at -M at time t, and n0(t) represents the noise sample data of the sensor located at 0 at time t. M (t) represents the noise sample data of the sensor located at M at time t; (·) T This is for the transpose operation; (·) * For conjugate operation; M is the index of the sensor's position in the uniform linear antenna array; K is the number of intermediate frequency signals from the down-conversion satellite containing different frequency components.

[0128] Furthermore, in the received signal matrix obtained according to the above steps, the matrix column data of the preset array sequence number L is selected for mean processing to determine multiple mean signal data. Based on all the mean signal data, a mean signal matrix with a dimension of 2M×1 can be obtained, the expression of which is:

[0129]

[0130] Where z(t) is the mean signal matrix composed of all mean signal data at time t; L is the preset array index; X(i) is the matrix column data of the i-th column z. -M z0 is the mean signal data corresponding to the sensor located at -M; z0 is the mean signal data corresponding to the sensor located at 0 ... M This represents the mean signal data corresponding to the sensor located at point M.

[0131] Furthermore, based on multiple mean signal data, a Toplitz matrix of dimension (M+1)×(M+1) is constructed, which is as follows:

[0132]

[0133] Where Z is the Topplitz matrix; z0 is the mean signal data corresponding to the sensor located at 0; z -1 This represents the mean signal data corresponding to the sensor located at -1; z -M z1 represents the mean signal data corresponding to the sensor located at -M; z2 represents the mean signal data corresponding to the sensor located at 1; z3 represents the mean signal data corresponding to the sensor located at -M. -M+1 z1 is the mean signal data corresponding to the sensor located at -M+1; z2 is the mean signal data corresponding to the sensor located at 2; z -M+2 This represents the mean signal data corresponding to the sensor located at -M+2; z M The mean signal data corresponding to the sensor located at point M; z M-1 This represents the mean signal data corresponding to the sensor located at M-1.

[0134] Based on the above, the Tollitz matrix can also be expressed as:

[0135]

[0136] Among them, Z y (t) is a matrix containing signal components; Z n (t) is a matrix containing noise components; y0 is the noise-free received data from the sensor located at 0; y1 is the noise-free received data from the sensor located at 1; y2 is the noise-free received data from the sensor located at 2; y M The noise-free received data from the sensor located at M; y-1 The noise-free received data from the sensor located at -1; y M-1 The noise-free received data from the sensor located at M-1; y -M For noise-free received data from the sensor located at -M; y -M+1 For noise-free received data from the sensor located at -M+1; y -M+2 n1 represents the noise-free received data from the sensor located at -M+2; n2 represents the noise data from the sensor located at 0; n1 represents the noise data from the sensor located at 1; n2 represents the noise data from the sensor located at 2; n M The noise data is from the sensor located at point M; n -1 The noise data for the sensor located at -1; n M-1 The noise data is from the sensor located at M-1; n -M This refers to the noise data of the sensor located at -M; n -M+1 This refers to the noise data of the sensor located at -M+1; n -M+2 This is the noise data for the sensor located at -M+2.

[0137] Furthermore, Z y (t) and Z n (t) can be represented as:

[0138]

[0139] Among them, Z y A is a matrix containing signal components; A is an array manifold matrix, A = [a(ω1), a(ω2), ..., a(ω...]]. K )], Z n S is a matrix containing noise components; p S is a positive definite diagonal matrix with dimensions K×K. P =diag(s1,…,s K ), s k >0, (k=1,…,K), s1 is the first element of a positive definite diagonal matrix, s k Let N be the k-th element of a positive definite diagonal matrix, and let diag(·) be the diagonal function; p Let N be the noise diagonal matrix. p =diag(n1,…,n) K ), where n1 is the first element of the noise diagonal matrix, n k Let be the k-th element of the noise diagonal matrix; (·) H For conjugate transpose operation; K is the number of incident intermediate frequency signals from downconverted satellites containing different frequency components;

[0140] Furthermore, due to S p Since Z is a full-rank matrix, its rank is the same as that of the array manifold matrix A. The array manifold matrix A is a Vandermonde matrix, and its rank cannot exceed K. Therefore, the rank of Z is K, which is the number of incident intermediate frequency signals from down-converting satellites containing different frequency components. Due to noise, the off-diagonal elements of Z are not conjugates. A conjugate smoothing operation is applied to ensure that the toplitz structure of the matrix remains unchanged even with noise. Finally, a matrix transformation is performed on the conjugate smoothed toplitz matrix to obtain the satellite real matrix.

[0141] In this embodiment, incident intermediate frequency (IF) signals from multiple downconversion satellites are acquired, and a satellite real number matrix is ​​constructed based on the incident IF signals from each downconversion satellite.

[0142] Step 202: Use the orthogonal triangular decomposition method to perform eigenvalue decomposition on the satellite real matrix to determine the signal-noise subspace.

[0143] It should be noted that subspace decomposition is a commonly used method for DOA (Direction of Arrival) estimation of signals. Common algorithms include the MUSIC (Multiple Signal Classification) algorithm and its various variants and improvements. The traditional MUSIC algorithm uses the covariance matrix of the received data for eigenvalue decomposition to separate the signal subspace and noise subspace. Then, based on the orthogonality between the signal steering vector and the noise subspace, a spatial spectrum is constructed. By scanning the spatial spectrum to obtain spectral peaks, the corresponding DOA estimate is obtained. Based on the orthogonality between the signal steering vector and the noise subspace to estimate parameters, this idea can be applied to frequency estimation.

[0144] In this embodiment, the orthogonal triangular decomposition method is used to perform eigenvalue decomposition on the satellite real matrix to determine the signal-noise subspace.

[0145] Step 203: Construct a frequency polynomial using a preset signal frequency vector and a noise subspace in the signal noise subspace.

[0146] Specifically, a frequency polynomial can be constructed based on the orthogonality between the noise subspace and the preset signal frequency vector.

[0147] In this embodiment, a frequency polynomial is constructed using a preset signal frequency vector and a noise subspace in the signal noise subspace.

[0148] Step 204: Solve the frequency polynomial to determine the intermediate frequency signal frequency.

[0149] Specifically, the value of ω can be obtained by solving the frequency polynomial, that is, by finding the roots of the polynomial.

[0150] In this embodiment, the frequency polynomial is solved to determine the intermediate frequency signal frequency.

[0151] Step 205: Perform a difference calculation on the intermediate frequency signal frequency and the preset carrier frequency to determine the estimated Doppler frequency shift value.

[0152] It should be noted that, by It can be known This allows us to derive the frequency estimate. The final frequency estimate (i.e., the intermediate frequency signal frequency) is the algebraic sum of the satellite navigation signal's carrier frequency and the Doppler frequency offset. The local carrier generator can provide the carrier frequency value (i.e., the preset carrier frequency). Subtracting the estimated frequency value from the local carrier frequency value yields the estimated Doppler frequency offset; where f is the frequency estimate, f... s The sampling frequency.

[0153] In this embodiment, the difference between the intermediate frequency signal frequency and the preset carrier frequency is calculated to determine the estimated Doppler frequency shift value.

[0154] Step 206: Based on the extended replication overlap method, the phase offset of the pseudocode is determined using the Doppler frequency shift estimate and the preset local pseudocode.

[0155] It should be noted that the sensitivity and reliability of GNSS receivers have always been important indicators for measuring their performance. If relatively accurate Doppler frequency offset and code phase estimates can be obtained during the satellite navigation signal acquisition process, the reliability of the subsequent satellite navigation signal tracking loop in tracking the signal can be improved.

[0156] Furthermore, the CM code has a length of 10,230, a code rate of 511.5 kcps, and a code period of 20 ms; the CL code has a length of 767,250, a code rate of 511.5 kbit / s, and a code period of 1.5 s. Using traditional parallel code phase acquisition algorithms would consume a significant amount of time. The acquisition of the pseudo-code phase of medium-length codes in satellite navigation signals mainly involves segmenting the local pseudo-code, then performing correlation operations segment by segment with the received pseudo-code. The basic process involves padding both the local and received pseudo-codes with zeros to N points, performing a Fast Fourier Transform (FFT), then taking the conjugate of the FFT of the local pseudo-code signal and multiplying it with the FFT of the received pseudo-code, followed by an inverse FFT to obtain the correlation value at each pseudo-code phase. Finally, a threshold decision is made on the correlation peaks to estimate the corresponding pseudo-code phase.

[0157] This invention utilizes the excellent autocorrelation properties of pseudocode through an extended replication overlap method. The algorithm's principle is to divide a long pseudocode segment into multiple segments of equal length and then add these segments together to obtain a new overlapping code segment. For example, dividing an N-point long pseudocode sequence into M K-point long sequences and adding these M K-point long sequences point by point yields a K-point long overlapping code segment. This K-point long sequence contains the information of the original M×K-point long code. Overlapping M segments reduces the number of Fast Fourier Transforms by a factor of M, while simultaneously expanding the time uncertainty range by a factor of M. Considering the superposition characteristics of noise, the number of overlapping signal segments should not be too large, otherwise the signal may be easily submerged in noise.

[0158] Furthermore, step 206 may include the following sub-steps:

[0159] S61. Carrier stripping is performed on the Doppler frequency shift estimate to generate a pseudo-code signal;

[0160] S62. Based on the data length of the pseudocode signal, the preset local pseudocode is truncated to generate multiple local pseudocode segments.

[0161] S63. Select local pseudocode segments sequentially according to the preset addition quantity and perform addition operations to determine multiple new local pseudocode sequences;

[0162] S64. Perform a fast Fourier transform on the pseudo-code signal to determine the frequency domain signal corresponding to the pseudo-code signal.

[0163] S65. Perform fast Fourier transform on each new local pseudocode sequence and take the conjugate to generate the local pseudocode transform sequence corresponding to each new local pseudocode sequence.

[0164] S66. Perform Fast Fourier Transform multiplication and Fast Fourier Transform inverse operations on each local pseudocode transform sequence and frequency domain signal respectively, and output the first pseudocode phase correlation value corresponding to each local pseudocode new sequence.

[0165] S67. Take the local pseudocode new sequence corresponding to the largest first pseudocode phase correlation value as the target local pseudocode new sequence.

[0166] S68. The local pseudocode segments with a preset sum of local pseudocode segments in the target local pseudocode new sequence are correlated with the pseudocode signal respectively, and the pseudocode phase and the correlation value of the second pseudocode phase corresponding to the preset sum of local pseudocode segments are output.

[0167] S69. Take the pseudocode phase corresponding to the largest second pseudocode phase correlation value as the pseudocode phase offset.

[0168] It should be noted that the steps of performing correlation operations between the local pseudocode segments with a preset sum of 2 in the target local pseudocode new sequence and the pseudocode signal are as follows: Assuming the preset sum of 2 is set to 2, firstly, perform a Fast Fourier Transform on the pseudocode signal to obtain the frequency domain signal corresponding to the pseudocode signal. Then, perform a Fast Fourier Transform on the two local pseudocode segments and take their conjugates to obtain the local pseudocode transform segments corresponding to the two local pseudocode segments. Then, multiply the two local pseudocode transform segments with the frequency domain signal by a Fast Fourier Transform, and then perform the inverse operation of the Fast Fourier Transform to obtain the correlation value between the pseudocode phase and the second pseudocode phase corresponding to the two local pseudocode segments.

[0169] In this embodiment, based on the extended replication overlap method, the Doppler frequency shift estimate and the preset local pseudocode are used to determine the pseudocode phase shift.

[0170] Step 207: Generate the target satellite navigation intermediate frequency signal using pseudocode phase offset and Doppler frequency shift estimates.

[0171] In this embodiment, pseudocode phase offset and Doppler frequency shift estimates are used to generate the target satellite navigation intermediate frequency signal.

[0172] For example, please refer to Figure 3After acquiring multiple incident intermediate frequency (IF) signals from down-conversion satellites, a uniform linear array is constructed using sensors. This array is then used to receive and sample the multiple incident signals (IF signals from down-conversion satellites), resulting in multiple output signals (down-conversion output signals). Based on each output signal, a received signal matrix is ​​constructed. The first L columns of data in the received signal matrix are then averaged. Based on the averaged signal, a Toplitz matrix is ​​constructed. The Toplitz matrix is ​​then subjected to conjugate smoothing and converted into a satellite real matrix. Based on the satellite real matrix, QR decomposition is performed to obtain the corresponding signal and noise subspaces. Based on the orthogonality of the preset signal frequency vector and noise subspace, a polynomial with the signal frequency as the unknown is constructed. The roots of the polynomial are found to obtain the IF signal frequency, which includes the carrier frequency and Doppler shift. The Doppler shift is estimated by subtracting the obtained frequency estimate from the carrier frequency known by the local carrier generator. The difference between the frequency of the local signal and the preset carrier frequency is calculated to determine the estimated Doppler frequency shift. After carrier stripping, the signal capture is switched from a two-dimensional search to a pseudo-code phase dimension search, i.e., carrier stripping is performed on the estimated Doppler frequency shift to generate a pseudo-code signal. Based on the data length of the pseudo-code signal, the preset local pseudo-code is divided into multiple local pseudo-code segments of the same length as the pseudo-code signal. Every two local pseudo-code segments are added to form multiple new local pseudo-code sequences. After performing a Fast Fourier Transform (FFT) on each new local pseudo-code sequence, the conjugate is taken. After performing an FFT on the pseudo-code signal, it is multiplied by the FFT of the conjugate of each new local pseudo-code sequence and then inverse FFT is taken to obtain the correlation value at each pseudo-code phase. To determine which superimposed new local pseudo-code sequence the correlation peak belongs to, the two local pseudo-code segments that have completed the superposition operation are correlated with the pseudo-code signal respectively. After obtaining the correlation peak, it can be determined which local pseudo-code segment it belongs to, and the corresponding phase is used as the code phase offset.

[0173] For comparison of technical effects, existing technologies can be used as a reference. Satellite navigation signals can be structurally divided into three layers: carrier wave, pseudocode, and data code. GNSS receivers acquire sufficient visible satellite signals and replicate and track these signals, enabling users to obtain the positioning, velocity, and timing information they are interested in. Therefore, reliably acquiring satellite navigation signals and improving the receiver's acquisition sensitivity are key to improving navigation and positioning performance.

[0174] Currently, the visibility of satellite navigation signals is determined by the presence of peaks in the acquisition correlation results. Traditional medium- and long-code satellite navigation signal acquisition algorithms primarily employ Fast Fourier Transform (FFT) to convert time-domain correlation operations into frequency-domain multiplication operations. This allows for the calculation of the correlation value and a determination of whether it exceeds the acquisition threshold. If it does, acquisition is successful; otherwise, the carrier frequency is adjusted for re-acquisition. Common methods include direct acquisition of medium- and long-code signals based on FFT and acquisition methods based on double-block zero-padding. Due to the long period of medium- and long-code signals, FFT-based acquisition methods require significant computation time. While double-block zero-padding improves acquisition speed compared to direct FFT-based methods by piecing together blocks of the medium- and long-code signal, it only utilizes the first half of the data, wasting computational resources. Therefore, a suitable method is urgently needed to address the slow acquisition speed and low sensitivity under strong noise interference issues of traditional medium- and long-code satellite navigation signal acquisition methods.

[0175] Based on the above, to address the problems of high computational complexity and low anti-interference capability in dynamic scenarios or high-noise backgrounds of existing long-code satellite navigation signal acquisition algorithms, this invention proposes a medium-long code satellite navigation signal acquisition method. First, the Toplitz matrix of the signal is decomposed using QR decomposition. Then, a polynomial is constructed and solved based on the orthogonality between the noise subspace and the steering vector to complete a one-dimensional estimation of the satellite navigation signal frequency. This transforms the two-dimensional search into a one-dimensional pseudo-code phase search, reducing the complexity of the two-dimensional search to a certain extent. Furthermore, the estimated frequency accuracy is high even in low carrier-to-noise ratio scenarios. An extended replication overlap method is employed to complete the one-dimensional estimation of the pseudo-code phase, which can complete the acquisition process faster than traditional acquisition algorithms. This provides a novel and high-performance acquisition method for GNSS receivers to acquire satellite navigation signals. This invention is scientific, feasible, and has engineering application value.

[0176] In summary, the medium-long code satellite navigation signal acquisition method proposed in this invention addresses the problems of low acquisition sensitivity, long computation time, and poor noise interference resistance in traditional medium-long code satellite navigation signal acquisition methods under low carrier-to-noise ratio scenarios. Most traditional medium-long code satellite navigation signal acquisition algorithms employ Fast Fourier Transform (FFT) to estimate carrier Doppler shift and pseudocode phase. Due to the long period of medium-long codes, the number of FFTs required increases, leading to a significant increase in computation time. Furthermore, traditional acquisition algorithms have poor noise interference resistance; the estimation accuracy in the frequency dimension depends on the data length of the FFT and the selected sampling frequency, resulting in low sensitivity in satellite navigation signal acquisition. Simultaneously, this invention reduces the complexity of the two-dimensional search process for medium-long code satellite navigation signal acquisition, improving both acquisition speed and sensitivity.

[0177] In this embodiment of the invention, a method for acquiring medium- and long-code satellite navigation signals is provided. The method involves acquiring the incident intermediate frequency (IF) signal from a down-converted satellite and constructing a satellite real matrix based on the IF signal. An orthogonal triangular decomposition method is used to perform eigenvalue decomposition on the satellite real matrix to determine the signal noise subspace. Based on the signal noise subspace and a preset signal frequency vector, a Doppler frequency shift estimate is determined. Using an extended replication overlap method, the Doppler frequency shift estimate and a preset local pseudocode are used to determine the pseudocode phase shift. The pseudocode phase shift and the Doppler frequency shift estimate are used to generate the satellite navigation IF signal. Based on the above scheme, the orthogonal triangular decomposition method and the extended replication overlap method are used to generate the satellite navigation IF signal from the acquired down-converted satellite incident IF signal. This process uses the orthogonal triangular decomposition method and the extended replication overlap method to estimate the frequency in one dimension and the pseudocode phase in one dimension, which reduces the complexity of the two-dimensional search and further reduces the acquisition time.

[0178] Please see Figure 4 , Figure 4 This is a structural block diagram of a medium-length code satellite navigation signal acquisition device provided in an embodiment of the present invention.

[0179] The present invention provides a medium- and long-code satellite navigation signal acquisition device, comprising:

[0180] The acquisition module 401 is used to acquire incident intermediate frequency signals from multiple downconverted satellites and construct a satellite real number matrix based on the incident intermediate frequency signals from each downconverted satellite.

[0181] Module 402 is used to perform eigenvalue decomposition on the satellite real matrix using the orthogonal triangular decomposition method to determine the signal-noise subspace;

[0182] Based on module 403, it is used to determine the Doppler frequency shift estimate based on the signal noise subspace and the preset signal frequency vector;

[0183] Extension module 404 is used to determine the pseudocode phase offset based on the extended replication overlap method, using Doppler frequency shift estimation and preset local pseudocode;

[0184] The generation module 405 is used to generate the target satellite navigation intermediate frequency signal by using pseudocode phase offset and Doppler frequency shift estimates.

[0185] Furthermore, module 401 is specifically used for:

[0186] The intermediate frequency signals incident from each down-conversion satellite are sampled by a pre-set uniform linear array, and multiple down-conversion output signals are output. The received signal matrix is ​​constructed using each down-conversion output signal.

[0187] In the received signal matrix, the matrix column data of the preset array sequence number position are selected for mean processing to determine multiple mean signal data;

[0188] Using the mean signal data, a Toplitz matrix is ​​constructed, and a conjugate smoothing operation is performed on the Toplitz matrix to generate a satellite complex matrix;

[0189] Perform matrix transformation on the satellite complex matrix to determine the satellite real matrix.

[0190] Furthermore, based on module 403, it is specifically used for:

[0191] A frequency polynomial is constructed using a preset signal frequency vector and a noise subspace within the signal noise subspace;

[0192] Solve the frequency polynomial to determine the intermediate frequency signal frequency;

[0193] The Doppler frequency shift estimate is determined by performing a difference calculation between the intermediate frequency signal frequency and the preset carrier frequency.

[0194] Furthermore, extension module 404 is specifically used for:

[0195] Carrier stripping is performed on the Doppler frequency shift estimate to generate a pseudo-code signal;

[0196] The preset local pseudocode is truncated according to the data length of the pseudocode signal to generate multiple local pseudocode segments.

[0197] Local pseudocode segments are selected sequentially according to a preset addition quantity for addition operations to determine multiple new local pseudocode sequences;

[0198] Perform a Fast Fourier Transform on the pseudocode signal to determine the frequency domain signal corresponding to the pseudocode signal;

[0199] Perform a Fast Fourier Transform on each new local pseudocode sequence and take its conjugate to generate the local pseudocode transform sequence corresponding to each new local pseudocode sequence.

[0200] Perform Fast Fourier Transform multiplication and Fast Fourier Transform inverse operations on each local pseudocode transform sequence and frequency domain signal respectively, and output the first pseudocode phase correlation value corresponding to each local pseudocode new sequence.

[0201] The local pseudocode sequence corresponding to the largest first pseudocode phase correlation value is taken as the target local pseudocode sequence.

[0202] The local pseudocode segments of the target local pseudocode new sequence with a preset sum of local pseudocode segments are correlated with the pseudocode signal, and the correlation value of the pseudocode phase and the second pseudocode phase corresponding to the preset sum of local pseudocode segments is output.

[0203] The pseudocode phase corresponding to the largest second pseudocode phase correlation value is taken as the pseudocode phase offset.

[0204] Furthermore, the frequency polynomial is specifically:

[0205]

[0206] in, a(z) = [1, z, ..., z M ] T ;

[0207] f(z) is the frequency polynomial; z is an element in the frequency search vector, j is the imaginary unit, ω is the digital frequency; M is the index of the sensor's position in the uniform linear antenna array; U is the unitary matrix; Q n Let K be the noise subspace, with dimensions (M+1)×((M+1)-K), where K is the number of incident intermediate frequency signals from down-converted satellites containing different frequency components; (·) T This is for the transpose operation; (·) H For conjugate transpose operation; a(z) is the frequency search vector; I is the identity matrix with a dimension of (M+1)×(M+1); J is the subdiagonal matrix with subdiagonal elements of 1 and other elements of 0, and its dimension is (M+1)×(M+1).

[0208] Furthermore, the calculation process for the satellite real matrix is ​​as follows:

[0209]

[0210] in, Let `real` be the real matrix of the satellite; `real(·)` is the real part function. Let J be the satellite complex matrix; imag(·) is the imaginary part function; J is the subdiagonal matrix, where the subdiagonal elements are 1 and the other elements are 0, and its dimension is (M+1)×(M+1).

[0211] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, and sub-modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0212] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the medium-long code satellite navigation signal acquisition method as described in any of the above embodiments.

[0213] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the medium-length code satellite navigation signal acquisition method as described in any of the above embodiments.

[0214] This invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the medium-length code satellite navigation signal acquisition method as described in any of the above embodiments.

[0215] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0216] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0217] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 invention.

Claims

1. A method of acquiring a medium-long code satellite navigation signal, characterized by, The method comprises the following steps: acquiring a plurality of down-converted satellite incident intermediate frequency signals, and constructing a satellite real number matrix according to each of the down-converted satellite incident intermediate frequency signals; performing eigenvalue decomposition on the satellite real number matrix by using an orthogonal triangular decomposition method to determine a signal-noise subspace; determining a Doppler frequency shift estimation value based on the signal-noise subspace and a preset signal frequency vector; determining a pseudo-code phase offset based on the Doppler frequency shift estimation value and a preset local pseudo-code by using an extended copy overlap method, which comprises the following steps: carrier stripping is performed on the Doppler frequency shift estimation value to generate a pseudo-code signal; the preset local pseudo-code is length-truncated according to the data length of the pseudo-code signal to generate a plurality of local pseudo-code sub-segments; a preset addition number of the local pseudo-code sub-segments are sequentially selected for addition operation to determine a plurality of new local pseudo-code sequences; fast Fourier transform is performed on the pseudo-code signal to determine a frequency domain signal corresponding to the pseudo-code signal; fast Fourier transform is performed on each of the new local pseudo-code sequences and the conjugate thereof is taken to generate a local pseudo-code transformed sequence corresponding to each of the new local pseudo-code sequences; fast Fourier transform multiplication and inverse operation of fast Fourier transform are respectively performed on each of the local pseudo-code transformed sequences and the frequency domain signal to output a first pseudo-code phase correlation value corresponding to each of the new local pseudo-code sequences; a new local pseudo-code sequence corresponding to the maximum first pseudo-code phase correlation value is taken as a target new local pseudo-code sequence; correlation operation is respectively performed on the target new local pseudo-code sequence and the pseudo-code signal by using the preset addition number of local pseudo-code sub-segments in the target new local pseudo-code sequence to output a pseudo-code phase corresponding to the preset addition number of local pseudo-code sub-segments and a second pseudo-code phase correlation value; a pseudo-code phase corresponding to the maximum second pseudo-code phase correlation value is taken as a pseudo-code phase offset; a target satellite navigation intermediate frequency signal is generated by using the pseudo-code phase offset and the Doppler frequency shift estimation value.

2. The method of claim 1, wherein, The step of constructing a satellite real number matrix according to each of the down-converted satellite incident intermediate frequency signals comprises the following steps: a plurality of down-converted output signals are output by sampling each of the down-converted satellite incident intermediate frequency signals through a preset uniform linear array, and a receive signal matrix is constructed by using each of the down-converted output signals; a plurality of mean signal data are determined by selecting matrix array data of a preset array serial number bit in the receive signal matrix for mean value processing; a Toeplitz matrix is constructed by using each of the mean signal data, and a satellite complex number matrix is generated by performing conjugate smoothing operation on the Toeplitz matrix; a satellite real number matrix is determined by performing matrix conversion on the satellite complex number matrix.

3. The method of claim 1, wherein, The step of determining a Doppler frequency shift estimation value based on the signal-noise subspace and a preset signal frequency vector comprises the following steps: a frequency polynomial is constructed by using a preset signal frequency vector and a noise subspace in the signal-noise subspace; a solution of the frequency polynomial is determined to determine an intermediate frequency signal frequency; a difference value operation is performed on the intermediate frequency signal frequency and a preset carrier frequency to determine a Doppler frequency shift estimation value.

4. The method of claim 3, wherein, The frequency polynomial is specifically ； is a frequency polynomial; is an element in a frequency search vector, is an imaginary unit, is a digital frequency; is a serial number of a position where a sensor in a uniform linear antenna array is located; is a unitary matrix; is a noise subspace, and a dimension of the noise subspace is , is a number of down-converted satellite incident intermediate frequency signals containing different frequency components; is a transpose operation; is a conjugate transpose operation; is a frequency search vector; is a unit matrix, and a dimension of the unit matrix is ; is a sub-diagonal matrix, sub-diagonal elements of the sub-diagonal matrix are 1, and other elements are 0, and a dimension of the sub-diagonal matrix is .

5. The method of claim 2, wherein, The calculation process of the satellite real number matrix is specifically ； wherein, is a satellite real matrix; is a real part function; is a satellite complex matrix; is an imaginary part function; is a sub-diagonal matrix, whose sub-diagonal elements are 1 and other elements are 0, and its dimension is .

6. A medium-long code satellite navigation signal acquisition device, applied to the medium-long code satellite navigation signal acquisition method of claim 1, characterized in that, comprises the following steps: An acquisition module is configured to acquire a plurality of down-converted satellite incident intermediate frequency signals, and construct a satellite real number matrix according to each of the down-converted satellite incident intermediate frequency signals; An adoption module is configured to adopt a QR decomposition method to perform eigen decomposition on the satellite real number matrix, and determine a signal noise subspace; A determination module is configured to determine a Doppler frequency shift estimation value based on the signal noise subspace and a preset signal frequency vector; An expansion module is configured to determine a pseudo code phase offset based on the Doppler frequency shift estimation value and a preset local pseudo code by using an extended copy overlap method; A generation module is configured to generate a target satellite navigation intermediate frequency signal by using the pseudo code phase offset and the Doppler frequency shift estimation value.

7. A computer device, characterized by The computer program / instruction is executed by the processor to implement the steps of the long code satellite navigation signal acquisition method according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the steps of the long code satellite navigation signal acquisition method according to any one of claims 1-5.

9. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the steps of the long code satellite navigation signal acquisition method according to any one of claims 1-5.

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