A GNSS baseband signal tracking method based on rolling time domain estimation

Abstract

The present invention relates to a GNSS baseband signal tracking method, and more specifically, to a GNSS baseband signal tracking method based on rolling time domain estimation. This method solves the technical problem that existing GNSS baseband signal tracking methods are susceptible to external factors, resulting in poor robustness. Based on rolling time domain estimation, the present invention employs an MHE filter to track carrier phase, and formulates the estimation problem as an optimization problem, eliminating the need for complex modeling of the tracking system. The constraints of the tracking system are directly expressed in the optimization objective, and state optimization is performed within a rolling time domain window. This method achieves superior tracking performance in real-world application environments of non-Gaussian and nonlinear systems, demonstrating strong robustness.

Description

Technical Field

[0001] The present invention relates to a GNSS baseband signal tracking method, and in particular to a GNSS baseband signal tracking method based on rolling time domain estimation. Background Art

[0002] The Global Navigation Satellite System (GNSS) is a space-based radio navigation and positioning system that can provide users with all-weather, high-precision positioning, navigation and timing services at any location on the Earth's surface or in near-Earth space. It has been widely used in resources and environment, surveying and mapping, disaster prevention and mitigation, power and telecommunications, transportation and other fields.

[0003] GNSS consists of satellites, control and monitoring systems, and receivers. The relative motion between the satellites and receivers causes Doppler shift in GNSS baseband signals. To overcome this Doppler shift, GNSS baseband signals must be tracked. Traditional GNSS baseband signal tracking methods use digital filtering or Kalman filtering, which are susceptible to various external factors, such as ionospheric scintillation, signal obstruction, and multipath. These factors can lead to signal degradation and even loss of lock, and are therefore less robust in real-world applications involving non-Gaussian and nonlinear systems. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problem that the existing GNSS baseband signal tracking method is easily affected by external factors, resulting in poor robustness, and to provide a GNSS baseband signal tracking method based on rolling time domain estimation.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A GNSS baseband signal tracking method based on rolling time domain estimation is characterized in that it includes the following steps:

[0007] Step 1: Based on the rolling time domain estimation, a tracking system model is established, and then initial data is given; the initial data includes the process noise covariance matrix Q, the measurement noise covariance matrix R, and the state error covariance matrix P 0. Initial state vector and the number of observation moments N in the rolling time domain window;

[0008] Step 2: The carrier oscillator is based on the initial state vector Generate two carrier signals and transmit them together with the GNSS baseband signal to the in-phase branch and the orthogonal branch respectively;

[0009] Step 3: The in-phase branch sequentially correlates and filters one carrier signal with the GNSS baseband signal to obtain an in-phase branch correlation signal; the quadrature branch quadrature couples the other carrier signal, and then sequentially correlates and filters it with the GNSS baseband signal to obtain a quadrature branch correlation signal;

[0010] Step 4: Superimpose the in-phase branch correlation signal and the quadrature branch correlation signal to obtain a correlation signal, which is sent to the phase detector. The phase detector detects the phase error of the correlation signal, obtains the current phase error, and transmits it to the MHE (Moving Horizon Estimation) filter.

[0011] Step 5: Based on the initial data given in step 1, the tracking system optimization objective function is constructed according to the tracking system model. The minimum value of the tracking system optimization objective function is solved according to the phase error at the current moment to obtain the optimal solution of the tracking system optimization objective function. Then, the state vector at the current moment is calculated according to the optimal solution of the optimization objective function.

[0012] Step 6: Calculate the state vector and state error covariance matrix at the next moment based on the state vector and state error covariance matrix at the current moment. Then, the carrier oscillator generates two carrier signals based on the state vector at the next moment and transmits them to the in-phase branch and the orthogonal branch respectively. Return to step 3 to achieve tracking of the GNSS baseband signal.

[0013] Furthermore, in step 1, the tracking system model is:

[0014]

[0015]

[0016] in, 、 Represent the current moment and the next moment respectively. is an integer, and ; 、 are the state vectors at the current moment and the next moment respectively, is the process noise at the current moment, is the measurement noise at the current moment, is the phase error at the current moment, is the state transfer matrix at the current moment, B is the input matrix, C is the observation matrix.

[0017] Furthermore, when the cumulative number of observation moments T≤N, step 5 is specifically as follows:

[0018] Step A5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model:

[0019]

[0020] in, represents the optimization objective function when T≤N, is the initial state vector estimate; 、 、 They are R 、 、 The inverse matrix of

[0021] Step A5.2: Find the minimum value of the tracking system optimization objective function based on the current phase error to obtain the optimal solution of the tracking system optimization objective function;

[0022] Step A5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula:

[0023]

[0024] in, The optimal solution of the state vector of the tracking system optimization objective function when T≤N, , which means the first The optimal solution of the process noise of the objective function of the tracking system optimization at each moment, It is the set of optimal solutions of process noise for optimizing the objective function of the tracking system at all observation times when T≤N.

[0025] Furthermore, when the cumulative number of observation moments T>N, step 5 is specifically as follows:

[0026] Step B5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model:

[0027]

[0028] in, represents the optimization objective function when T>N, For the The state vector at time t, For the The state vector estimate at time t, For the The state error covariance matrix at time t;

[0029] Step B5.2: Based on the phase error at the current moment, find the minimum value of the tracking system optimization objective function to obtain the optimal solution of the tracking system optimization objective function;

[0030] Step B5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula:

[0031]

[0032] in, The optimal solution of the state vector of the tracking system optimization objective function when T>N, , indicating that when T>N, the The optimal solution of the process noise of the objective function of the tracking system optimization at each moment, It is the set of optimal solutions of process noise of the tracking system optimization objective function in all observation moments in the range [TN, T] when T>N.

[0033] Furthermore, in step 6, the state vector state and error covariance matrix at the next moment are calculated by the following formula:

[0034]

[0035] in, 、 are the state error covariance matrices of the current moment and the next moment respectively; 、 Respectively 、 The transposed matrix of .

[0036] Furthermore, in step 2, the in-phase branch includes a multiplier and a low-pass filter connected in sequence; the input end of the multiplier is used to receive the GNSS baseband signal and the carrier signal, and the output end of the low-pass filter is used to output the in-phase branch related signal;

[0037] In step 2, the orthogonal branch includes a 90° phase converter, a multiplier and a low-pass filter connected in sequence; the input end of the 90° phase converter is used to receive the carrier signal, the 90° phase converter is used to orthogonally couple the carrier signal, the input end of the multiplier is used to receive the GNSS baseband signal and the carrier signal after orthogonal coupling, and the output end of the low-pass filter is used to output the orthogonal branch related signal.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention provides a GNSS baseband signal tracking method based on rolling time domain estimation. Based on rolling time domain estimation, an MHE filter is used to complete carrier phase tracking. The estimation problem is expressed as an optimization problem, eliminating the need for complex modeling of the tracking system. At the same time, the constraints of the tracking system are directly expressed in the optimization objective, and state optimization is performed within a rolling time domain window. In the real application environment of non-Gaussian and nonlinear systems, superior tracking performance can be obtained, and the method has strong robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a method according to an embodiment of the present invention;

[0041] Figure 2 A comparison diagram of the code loop discriminator error standard deviation curves obtained by using the embodiment of the present invention and the existing GNSS baseband signal tracking method based on Kalman filtering;

[0042] Figure 3 The figure is a comparison chart of the standard deviation change curves of the phase detection output error obtained by using the embodiment of the present invention and the existing GNSS baseband signal tracking method based on Kalman filtering. DETAILED DESCRIPTION

[0043] To further clarify the objectives, advantages, and features of the present invention, the following describes in further detail a GNSS baseband signal tracking method based on rolling time-domain estimation, as proposed in the present invention, in conjunction with the accompanying drawings and specific embodiments. It should be noted that GNSS baseband signal tracking includes carrier loop tracking and code loop tracking. Since code loop tracking and carrier loop tracking are similar, this invention only describes the method and structure of carrier loop tracking.

[0044] A GNSS baseband signal tracking method based on rolling time domain estimation, such as Figure 1 As shown, the following steps are included:

[0045] Step 1: Based on the rolling time domain estimation, the tracking system model is established, and then the process noise covariance matrix Q, the measurement noise covariance matrix R, and the state error covariance matrix are given. P 0. Initial state vector And the number of observation moments in the rolling time domain window N. Among them, the tracking system model is:

[0046]

[0047]

[0048] in, 、 Represent the current moment and the next moment respectively. is an integer, and ; 、 are the state vectors at the current moment and the next moment respectively, is the process noise at the current moment, is the measurement noise at the current moment, is the phase error at the current moment, is the state transfer matrix at the current moment, B is the input matrix, C is the observation matrix.

[0049] Step 2: The carrier oscillator is based on the initial state vector Two carrier signals are generated and transmitted together with the GNSS baseband signal to the in-phase branch and the quadrature branch, respectively. The in-phase branch includes a multiplier and a low-pass filter connected in sequence. The multiplier input is used to receive the GNSS baseband signal and the carrier signal, and the low-pass filter output is used to output the in-phase branch-related signal. The quadrature branch includes a 90° phase transformer, a multiplier, and a low-pass filter connected in sequence. The 90° phase transformer input is used to receive the carrier signal, the 90° phase transformer is used to orthogonally couple the carrier signal, the multiplier input is used to receive the GNSS baseband signal and the carrier signal after orthogonal coupling, and the low-pass filter output is used to output the quadrature branch-related signal.

[0050] Step 3: The in-phase branch uses a multiplier and a low-pass filter to correlate and filter one carrier signal with the GNSS baseband signal in sequence to obtain the in-phase branch correlation signal; the orthogonal branch uses a 90° phase transformer to orthogonally couple the other carrier signal, and then uses a multiplier and a low-pass filter to correlate and filter the orthogonally coupled carrier signal with the GNSS baseband signal in sequence to obtain the orthogonal branch correlation signal.

[0051] Step 4: Use an adder to superimpose the in-phase branch correlation signal and the orthogonal branch correlation signal to obtain a correlation signal, and send it to the phase detector. The phase detector performs phase error detection on the correlation signal to obtain the phase error at the current moment and transmits it to the MHE filter.

[0052] Step 5. Based on the initial data given in step 1, the tracking system optimization objective function is constructed according to the tracking system model, and the minimum value of the tracking system optimization objective function is solved according to the phase error at the current moment to obtain the optimal solution of the tracking system optimization objective function. Then, according to the optimal solution of the optimization objective function, the state vector at the current moment is calculated.

[0053] When the cumulative number of observation moments T≤N, step 5 is as follows:

[0054] Step A5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model:

[0055]

[0056] in, represents the optimization objective function when T≤N, is the initial state vector estimate; 、 、 They are R 、 、 The inverse matrix of

[0057] Step A5.2: Find the minimum value of the tracking system optimization objective function based on the current phase error to obtain the optimal solution of the tracking system optimization objective function;

[0058] Step A5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula:

[0059]

[0060] in, The optimal solution of the state vector of the tracking system optimization objective function when T≤N, , which means the first The optimal solution of the process noise of the objective function of the tracking system optimization at each moment, It is the set of optimal solutions of process noise for optimizing the objective function of the tracking system at all observation times when T≤N.

[0061] As the number of accumulated observation moments T increases, when T>N, continue to perform the following specific step 5:

[0062] Step B5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model:

[0063]

[0064] in, represents the optimization objective function when T>N, For the The state vector at time t, For the The state vector estimate at time t, For the The state error covariance matrix at time t;

[0065] Step B5.2: Based on the phase error at the current moment, find the minimum value of the tracking system optimization objective function to obtain the optimal solution of the tracking system optimization objective function;

[0066] Step B5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula:

[0067]

[0068] in, The optimal solution of the state vector of the tracking system optimization objective function when T>N, , indicating that when T>N, the The optimal solution of the process noise of the objective function of the tracking system optimization at each moment, It is the set of optimal solutions of process noise of the tracking system optimization objective function in all observation moments in the range [TN, T] when T>N.

[0069] Step 6: Calculate the state vector and state error covariance matrix at the next moment based on the state vector and state error covariance matrix at the current moment. Then, the carrier oscillator generates two carrier signals based on the state vector at the next moment and transmits them to the in-phase branch and the orthogonal branch respectively. Return to step 3 to achieve GNSS baseband signal tracking. The state vector state and error covariance matrix at the next moment are calculated using the following formula:

[0070]

[0071]

[0072] in, 、 are the state error covariance matrices of the current moment and the next moment respectively; 、 Respectively 、 The transposed matrix of .

[0073] The following experiments verify the effectiveness of the method of this embodiment. The experimental environment is set as follows: using the Cornell ionospheric scintillation model, the corresponding ionospheric scintillation sequence is simulated, and by setting the ionospheric parameters, the amplitude scintillation sequence and phase scintillation sequence of different scintillation intensities are obtained, and then the amplitude scintillation sequence is multiplied by the normal signal amplitude value to generate the final signal amplitude. At the same time, the phase scintillation sequence is superimposed on the normal phase to form the final signal phase. The final signal amplitude, the final signal phase and the ionospheric scintillation sequence are combined to finally form the required ionospheric scintillation signal, which is saved as a binary file. Among them, the ionospheric parameters for weak scintillation are set to: S4=0.1, tau0=0.7; S4=0.2, tau0=0.6; S4=0.3, tau0=0.5; S4=0.4, tau0=0.4. The ionospheric parameters for medium scintillation are set to: S4=0.5, tau0=0.3; S4=0.6, tau0=0.2. The parameters for the severely scintillated ionosphere were set to: S4 = 0.7, tau0 = 0.1. The signal duration was 5 minutes. The software receiver parameters were set to a coherent integration time of 2 ms, a non-coherent lead-minus-lag power method for the code loop discriminator, and a code tracking loop lead-lag spacing D of 1 / 8 chips.

[0074] The method of this embodiment and the existing GNSS baseband signal tracking method based on Kalman filtering are used to track the GNSS baseband signal in a set test environment, and the standard deviation of the code ring discriminator error under different scintillation intensities as shown in Table 1 and the standard deviation of the code ring discriminator error under different scintillation intensities as shown in Table 1 are obtained. Figure 2 The error standard deviation curve of the code ring discriminator is shown in FIG. Figure 3 The curve showing the standard deviation of the phase detection output error is shown.

[0075] Table 1 Standard deviation of code ring discriminator error under different scintillation intensities

[0076]

[0077] like Figure 2 As shown in Table 1, the standard deviation of the code discriminator error gradually increases with increasing scintillation intensity. For the Kalman filter-based GNSS baseband signal tracking method, when the amplitude scintillation index S4 reaches 0.7, the standard deviation of the code ring discriminator error exceeds the tracking threshold, and the loop is considered to have lost lock. In comparison, the code discriminator error standard deviation of the present embodiment is smaller than that of the Kalman filter-based GNSS baseband signal tracking method at various scintillation intensities. Under severe scintillation conditions, the code discriminator error standard deviation is reduced by 9.6% using the present embodiment. Even when the amplitude scintillation index S4 is 0.7, the code discriminator error standard deviation remains below the tracking threshold using the present embodiment, maintaining good tracking of the GNSS baseband signal.

[0078] like Figure 3 As shown, as the amplitude flicker index S4 increases, the standard deviation of the phase detection output error gradually increases. When the amplitude flicker index S4 is less than 0.4, the flicker is weak, and the standard deviation of the phase detection output error increases slowly. However, when the amplitude flicker index S4 exceeds 0.5, the flicker intensity gradually increases, and the standard deviation of the phase detection output error increases dramatically. Therefore, the standard deviation of the phase detection output error during flicker using this implementation is also smaller than that of the GNSS baseband signal tracking method based on Kalman filtering. Therefore, when flicker is present, the tracking accuracy of this implementation method is significantly better than that of the GNSS baseband signal tracking method based on Kalman filtering.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. A GNSS baseband signal tracking method based on rolling time domain estimation, characterized in that: The following steps are involved: Step 1: Based on the rolling horizon estimation, a tracking system model is established and initial data is given; The initial data includes the process noise covariance matrix Q, the measurement noise covariance matrix R, the state error covariance matrix P 0. Initial state vector and the number of observation moments N in the rolling time domain window; Step 2: The carrier oscillator is based on the initial state vector Generate two carrier signals and transmit them together with the GNSS baseband signal to the in-phase branch and the orthogonal branch respectively; Step 3: The in-phase branch correlates and filters the carrier signal and the GNSS baseband signal in sequence to obtain an in-phase branch correlation signal; The orthogonal branch performs orthogonal coupling on the other carrier signal, and then correlates and filters it with the GNSS baseband signal in sequence to obtain the orthogonal branch correlation signal; Step 4: Superimpose the in-phase branch correlation signal and the quadrature branch correlation signal to obtain a correlation signal, and send it to the phase detector. The phase detector performs phase error detection on the correlation signal to obtain the phase error at the current moment and transmits it to the MHE filter. Step 5: Based on the initial data given in step 1, the tracking system optimization objective function is constructed according to the tracking system model. The minimum value of the tracking system optimization objective function is solved according to the phase error at the current moment to obtain the optimal solution of the tracking system optimization objective function. Then, the state vector at the current moment is calculated according to the optimal solution of the optimization objective function. Step 6: Calculate the state vector and state error covariance matrix at the next moment based on the state vector and state error covariance matrix at the current moment. Then, the carrier oscillator generates two carrier signals based on the state vector at the next moment and transmits them to the in-phase branch and the orthogonal branch respectively. Return to step 3 to achieve tracking of the GNSS baseband signal.

2. The GNSS baseband signal tracking method based on rolling time domain estimation according to claim 1, characterized in that: In step 1, the tracking system model is: ； ； in, 、 Represent the current moment and the next moment respectively. is an integer, and ; 、 are the state vectors at the current moment and the next moment respectively, is the process noise at the current moment, is the measurement noise at the current moment, is the phase error at the current moment, is the state transfer matrix at the current moment, B is the input matrix, C is the observation matrix.

3. The GNSS baseband signal tracking method based on rolling time domain estimation according to claim 2, characterized in that: When the cumulative number of observation moments T≤N, step 5 is as follows: Step A5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model: ； in, represents the optimization objective function when T≤N, is the initial state vector estimate; 、 、 They are R 、 、 The inverse matrix of Step A5.2: Find the minimum value of the tracking system optimization objective function based on the current phase error to obtain the optimal solution of the tracking system optimization objective function; Step A5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula: ； in, The optimal solution of the state vector of the tracking system optimization objective function when T≤N, , represents the optimal solution of the process noise of the tracking system optimization objective function at the i-th moment among all observation moments when T≤N, It is the set of optimal solutions of process noise for optimizing the objective function of the tracking system at all observation times when T≤N.

4. The GNSS baseband signal tracking method based on rolling time domain estimation according to claim 3, characterized in that: When the cumulative number of observation moments T>N, step 5 is specifically as follows: Step B5.1: Based on the initial data given in step 1, construct the tracking system optimization objective function as shown in the following formula according to the tracking system model: ； in, represents the optimization objective function when T>N, For the The state vector at time t, For the The state vector estimate at time t, For the The state error covariance matrix at time t; Step B5.2: Based on the phase error at the current moment, find the minimum value of the tracking system optimization objective function to obtain the optimal solution of the tracking system optimization objective function; Step B5.3: Based on the optimal solution of the tracking system optimization objective function, calculate the state vector at the current moment using the following formula: ； in, The optimal solution of the state vector of the tracking system optimization objective function when T>N, , indicating that when T>N, the The optimal solution of the process noise of the objective function of the tracking system optimization at each moment, It is the set of optimal solutions of process noise of the tracking system optimization objective function in all observation moments in the range [TN, T] when T>N.

5. A GNSS baseband signal tracking method based on rolling time domain estimation according to any one of claims 2 to 4, characterized in that: In step 6, the state vector state and error covariance matrix at the next moment are calculated by the following formula: ； ； in, 、 are the state error covariance matrices of the current moment and the next moment respectively; 、 Respectively 、 The transposed matrix of .

6. The GNSS baseband signal tracking method based on rolling time domain estimation according to claim 5, characterized in that: In step 2, the in-phase branch includes a multiplier and a low-pass filter connected in sequence; the input end of the multiplier is used to receive the GNSS baseband signal and the carrier signal, and the output end of the low-pass filter is used to output the in-phase branch related signal; In step 2, the orthogonal branch includes a 90° phase converter, a multiplier and a low-pass filter connected in sequence; the input end of the 90° phase converter is used to receive the carrier signal, the 90° phase converter is used to orthogonally couple the carrier signal, the input end of the multiplier is used to receive the GNSS baseband signal and the carrier signal after orthogonal coupling, and the output end of the low-pass filter is used to output the orthogonal branch related signal.

Images