A high-positioning-precision vehicle adaptive navigation method and vehicle-mounted navigator

Abstract

The present invention discloses a high-positioning-precision vehicle adaptive navigation method and a vehicle-mounted navigator. The method includes: constructing a satellite navigation signal neural network and an interference detection model; using a receiving unit to receive the current satellite navigation signal, using the target satellite navigation signal neural network and searching for the optimal solution for the current satellite navigation signal according to a fitness algorithm, or weighting a second satellite navigation signal using a weighted parameter matrix; and using a fusion filter to fuse the satellite navigation signal with an inertial navigation signal to obtain a predicted state signal of the vehicle at the current moment. This method improves navigation positioning accuracy, detects interference to the navigation satellite signal using an interference detection model, and weights the noise covariance matrix of the satellite navigation signal using a weighted parameter matrix, significantly reducing the impact of satellite navigation signal anomalies on navigation system performance and improving navigation accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted navigators, and in particular to a high-positioning-precision vehicle adaptive navigation method and a vehicle-mounted navigator. Background Art

[0002] In-car navigation services, as an emerging industry, are gaining widespread attention and exhibiting broad application prospects as user demand grows. Currently, mainstream in-car navigation terminals on the market primarily offer user-friendly route planning and extended features such as live voice prompts. Key features of in-car navigation terminals include: button-based selection of incremental updates, positioning capabilities, real-time download of remote electronic maps, real-time display of positioning information and map data for navigation, and the ability to power down the main control chip to reduce power consumption, which can be instantly awakened by pressing a corresponding function key. In-car navigation systems regularly update map data: service providers update map data at regular intervals, and users periodically download updated maps from the service provider.

[0003] In real-time navigation, the landing signal power of the global satellite navigation system in the vehicle-mounted navigation terminal is low and susceptible to various interferences, resulting in poor availability. Especially in complex urban environments, there are multipath interference, satellite signals are blocked or satellites are not visible, etc., which causes the positioning error to increase sharply. When the satellite is not visible or the signal is frequently and temporarily lost, the filtering model of the navigation system cannot perceive in real time and uses signals with deteriorated measurement performance, resulting in a deterioration of the combined filtering effect or unusable results, thereby reducing the positioning accuracy of the navigator. Summary of the Invention

[0004] The positioning accuracy of the navigation system of current car navigation systems decreases when the satellite is blocked and cannot be seen or is subject to multipath interference.

[0005] To address the above problems, a high-positioning-precision vehicle adaptive navigation method and a vehicle-mounted navigation device are proposed. By constructing a satellite navigation signal neural network and adopting a fitness algorithm to obtain the global optimal solution when the satellite navigation signal loses lock, the navigation positioning accuracy is improved. The interference to the navigation satellite signal is detected by an interference detection model, and the noise covariance matrix of the satellite navigation signal is weighted using a weighted parameter matrix. This greatly reduces the impact of satellite navigation signal anomalies on the navigation system performance and improves navigation accuracy.

[0006] A high positioning accuracy vehicle adaptive navigation method, comprising:

[0007] Step 100: construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain an autocorrelation function of the satellite navigation signal, and use the autocorrelation function to construct an interference detection model;

[0008] Step 200: Receive the current satellite navigation signal using a receiving unit and make a judgment:

[0009] If the satellite navigation signal is locked, using the target satellite navigation signal neural network and searching for an optimal solution of the satellite navigation signal at the current moment according to a fitness algorithm to obtain a first satellite navigation signal at the current moment;

[0010] If the satellite navigation signal is not lost, obtaining a second satellite navigation signal at the current moment, performing signal anomaly detection on the second satellite navigation signal using the interference detection model, and if a signal anomaly exists, obtaining a weighting parameter matrix corresponding to each satellite using the interference detection model, and weighting the second satellite navigation signal using the weighting parameter matrix to obtain a third satellite navigation signal;

[0011] Step 300: Obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at the current moment, and use the fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain a predicted state signal of the vehicle at the current moment.

[0012] In conjunction with the high positioning accuracy vehicle adaptive navigation method according to the first aspect of the present invention, in a first possible implementation, step 100 includes:

[0013] Step 110: Integrate the received satellite navigation signal with the locally stored pseudo code and carrier signal to obtain the autocorrelation function;

[0014] Step 120: performing phase tracking on the pseudocode, respectively calculating and obtaining the leading branch slope and the lagging branch slope of the pseudocode, and performing real part operation and imaginary part operation on the autocorrelation function to obtain the real part function and the imaginary part function respectively;

[0015] Step 130: Obtain the interference detection model using the leading branch slope, the lagging branch slope, the real part function, and the imaginary part function.

[0016] In conjunction with the high positioning accuracy vehicle adaptive navigation method described in the first aspect of the present invention, in a second possible implementation, step 200 includes:

[0017] Step 210: The target satellite navigation signal neural network randomly generates a plurality of first initial solutions;

[0018] Step 220: adaptively calculate and obtain the search step length based on the fitness of two adjacent searched solutions;

[0019] Step 230: Searching around the current first initial solution according to the search step size. If the fitness of the searched solution is greater than the fitness of the current first initial solution, the searched solution is used as the current global optimal solution. Searching is performed around each of the first initial solutions to obtain a first global optimal solution set.

[0020] Step 240: Perform optimal search using each solution in the first global optimal solution set as a second initial solution;

[0021] Step 250: Repeat steps 210-240 until a global optimal solution is found.

[0022] In combination with the second possible implementation manner of the first aspect of the present invention, in a third possible implementation manner, step 220 includes:

[0023] Step 221: Obtain the first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution;

[0024] Step 222: Obtain a fitness adjustment factor, and use the fitness adjustment factor, the first fitness, and the second fitness to obtain the search step length.

[0025] In combination with the third possible implementation manner of the first aspect of the present invention, in a fourth possible implementation manner, step 200 further includes:

[0026] Step 260: Obtain the satellite navigation signal measurement value of the receiving unit at the current moment;

[0027] Step 270: Obtain a nonlinear measurement function of the satellite navigation signal and a first noise covariance matrix thereof;

[0028] Step 280: Obtain the second satellite navigation signal by calculation according to the satellite navigation signal measurement value, the nonlinear measurement function, and the first noise covariance matrix.

[0029] In conjunction with the fourth possible implementation manner of the first aspect of the present invention, in a fifth possible implementation manner, step 200 further includes:

[0030] Step 260: Obtain the deviation value of each satellite using the interference detection model;

[0031] Step 270: Normalize the deviation value of each satellite to obtain the weighted parameter matrix.

[0032] In conjunction with the fourth possible implementation manner of the first aspect of the present invention, in a fifth possible implementation manner, step 200 further includes:

[0033] Step 280: weighting the first noise covariance matrix using the weighting parameter matrix to obtain a second noise covariance matrix;

[0034] Step 290: Utilize the second noise covariance matrix to calculate and obtain the third satellite navigation signal.

[0035] In a second aspect, a vehicle-mounted navigation system includes:

[0036] Get module;

[0037] Satellite signal module;

[0038] Fusion module;

[0039] The acquisition module is used to construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain an autocorrelation function of the satellite navigation signal, and construct an interference detection model using the autocorrelation function;

[0040] The satellite signal module is used to receive the current satellite navigation signal using a receiving unit and determine:

[0041] If the satellite navigation signal is locked, using the target satellite navigation signal neural network and searching for an optimal solution of the satellite navigation signal at the current moment according to a fitness algorithm to obtain a first satellite navigation signal at the current moment;

[0042] If the satellite navigation signal is not lost, obtaining a second satellite navigation signal at the current moment, performing signal anomaly detection on the second satellite navigation signal using the interference detection model, and if a signal anomaly exists, obtaining a weighting parameter matrix corresponding to each satellite using the interference detection model, and weighting the second satellite navigation signal using the weighting parameter matrix to obtain a third satellite navigation signal;

[0043] The fusion module is used to obtain a fusion filter, use the inertial module to obtain the inertial navigation signal at the current moment, and use the fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain the predicted state signal of the vehicle at the current moment.

[0044] In conjunction with the vehicle-mounted navigation device according to the second aspect of the present invention, in a first possible implementation, the acquisition module includes:

[0045] an integration unit, configured to integrate the received satellite navigation signal with the locally stored pseudo code and carrier signal to obtain an autocorrelation function of the satellite navigation signal;

[0046] a first calculation unit, configured to perform phase tracking on the pseudocode, respectively calculate and obtain the leading branch slope and the lagging branch slope of the pseudocode, and perform a real part operation and an imaginary part operation on the autocorrelation function to obtain a real part function and an imaginary part function, respectively;

[0047] A construction unit is used to obtain the interference detection model using the leading branch slope, the lagging branch slope, the real part function and the imaginary part function.

[0048] In conjunction with the first possible implementation manner of the second aspect of the present invention, in a second possible implementation manner, the satellite signal module includes:

[0049] The second calculation unit is used to adaptively calculate and obtain the search step length according to the fitness of two adjacent searched solutions;

[0050] A search unit is used to search around the current first initial solution according to the search step size. If the fitness of the searched solution is greater than the fitness of the current first initial solution, the searched solution is used as the current global optimal solution, and a search is performed near each of the first initial solutions to obtain a first global optimal solution set. Each solution in the first global optimal solution set is used as a second initial solution for optimal search until the global optimal solution is found.

[0051] In conjunction with the second possible implementation manner of the second aspect of the present invention, in a third possible implementation manner, the second computing unit is further configured to:

[0052] Obtain the first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution, obtain a fitness adjustment factor, and use the fitness adjustment factor, the first fitness, and the second fitness to obtain the search step size.

[0053] The high-positioning-precision vehicle adaptive navigation method and vehicle-mounted navigation device described in the present invention improve navigation positioning accuracy by constructing a satellite navigation signal neural network and adopting a fitness algorithm to obtain a global optimal solution when the satellite navigation signal loses lock. The interference to the navigation satellite signal is detected by an interference detection model, and the noise covariance matrix of the satellite navigation signal is weighted using a weighted parameter matrix. This greatly reduces the impact of satellite navigation signal anomalies on navigation system performance and improves navigation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 This is a schematic diagram of the first step of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0056] Figure 2 This is a schematic diagram of the second step of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0057] Figure 3 This is a schematic diagram of the third step of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0058] Figure 4 This is a schematic diagram of the fourth step of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0059] Figure 5 This is a schematic diagram of step 5 of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0060] Figure 6 This is a sixth schematic diagram of the method for adaptive navigation of a vehicle with high positioning accuracy according to the present invention;

[0061] Figure 7 This is a schematic diagram of step 7 of the high positioning accuracy vehicle adaptive navigation method of the present invention;

[0062] Figure 8 It is a structural schematic diagram of the vehicle-mounted navigation system of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0064] The positioning accuracy of the navigation system of current car navigation systems decreases when the satellite is blocked and cannot be seen or is subject to multipath interference.

[0065] Aiming at the above problems, a vehicle adaptive navigation method with high positioning accuracy and a vehicle navigator are proposed.

[0066] A high positioning accuracy vehicle adaptive navigation method, such as Figure 1 , Figure 1 This is a first schematic diagram of the steps of the high positioning accuracy vehicle adaptive navigation method of the present invention; it includes:

[0067] Step 100: Construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain an autocorrelation function of the satellite navigation signal, and construct an interference detection model using the autocorrelation function.

[0068] In this embodiment, the satellite navigation signal neural network adopts a convolutional neural network structure, and after training, the prediction value of the target navigation neural network is made more accurate.

[0069] In this embodiment, a combined system of a global satellite navigation system and a strapdown inertial navigation system is used to locate the satellite navigation signal of the global satellite navigation system at the current time t as X n .

[0070] At the current time t, the various state quantities of the satellite navigation signal are:

[0071] X n ={x1, x2, ..., x n};

[0072] All measurements obtained by the receiving units of the inertial navigation system and the global satellite navigation system are:

[0073] Z n ={z1, z2, ..., z n}.

[0074] In a preferred embodiment, Figure 2 , Figure 2 This is a second schematic diagram of the steps of the high positioning accuracy vehicle adaptive navigation method of the present invention; step 100 includes:

[0075] Step 110: Integrate the received satellite navigation signal with the locally stored pseudocode and carrier signal to obtain an autocorrelation function. Step 120: Phase track the pseudocode, calculate the leading branch slope and the lagging branch slope of the pseudocode, and perform real and imaginary operations on the autocorrelation function to obtain the real function and the imaginary function, respectively. Step 130: Utilize the leading branch slope, the lagging branch slope, the real function, and the imaginary function to obtain an interference detection model.

[0076] In this embodiment, it is assumed that a satellite navigation signal received by the receiving unit includes a direct signal and a multipath reflection signal. The receiving unit performs frequency conversion processing on the received signal to obtain a satellite navigation signal x(t) at the current moment. The satellite navigation signal x(t) is integrated with the local pseudo code and the carrier signal over time T, and the noise term is ignored to obtain the autocorrelation function x(ε) of the satellite navigation signal x(t).

[0077] In this embodiment, a code delay lock loop (DLL) is used to track the pseudo code phase of the received signal, and the output result of the phase detector is used to estimate the pseudo code phase error.

[0078] The imaginary part of the autocorrelation function x(ε) is taken to obtain K1=Im[x(ε)], and the real part is taken to obtain K2=Re[x(ε)]. The slopes of the leading branch and the delayed branch of the loop are calculated, and the phase detector factor μ is obtained from the slopes of the leading branch and the delayed branch. From the above, the interference detection model D can be obtained as follows:

[0079] D=|μ*(K1 / K2)| (1),

[0080] The principle of detecting satellite navigation signals using the above-mentioned interference detection model D is that if the satellite navigation signal has no multipath signal interference and the loop tracks the pseudo-code phase temperature, the phase detector factor μ is zero, the real part K2 is much larger than the imaginary part K1, and D is close to 0. If multipath interference exists and the loop is unstable in tracking the pseudo-code phase, the phase detector factor μ is not zero, the real part K2 and the imaginary part K1 are not much different in size, and the D value is not zero. The stronger the interference signal, the larger the D value.

[0081] Step 200: Receive the current satellite navigation signal using a receiving unit and make a judgment: if the satellite navigation signal is locked, use the target satellite navigation signal neural network and the fitness algorithm to search for the optimal solution of the current satellite navigation signal to obtain the first satellite navigation signal at the current moment; if the satellite navigation signal is not locked, obtain the second satellite navigation signal at the current moment, use the interference detection model to perform signal anomaly detection on the second satellite navigation signal, and if there is a signal anomaly, use the interference detection model to obtain the weighted parameter matrix corresponding to each satellite, and use the weighted parameter matrix to weight the second satellite navigation signal to obtain the third satellite navigation signal.

[0082] In this embodiment, the satellite navigation is first tested to see if it has lost lock. If so, the trained target satellite navigation signal neural network is used to obtain the optimal solution and the satellite navigation signal is predicted and output.

[0083] In a preferred embodiment, Figure 3 , Figure 3 This is a third schematic diagram of the steps of the high-positioning-precision vehicle adaptive navigation method of the present invention; step 200 includes: step 210, the target satellite navigation signal neural network randomly generates multiple first initial solutions; step 220, adaptively calculates and obtains the search step length based on the fitness of the solutions searched twice adjacently; step 230, searches in the neighborhood of the current first initial solution according to the search step length, and if the fitness of the searched solution is greater than the fitness of the current first initial solution, then uses the searched solution as the current global optimal solution, searches near each first initial solution, and obtains a first global optimal solution set; step 240, uses each solution in the first global optimal solution set as the second initial solution for optimal search; step 250, repeats steps 210-240 until the global optimal solution is found.

[0084] In a preferred embodiment, Figure 4 , Figure 4 This is a fourth schematic diagram of the steps of the high-positioning-precision vehicle adaptive navigation method of the present invention; step 220 includes: step 221, obtaining the first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution; step 222, obtaining the fitness adjustment factor, and using the fitness adjustment factor, the first fitness, and the second fitness to obtain the search step length.

[0085] In this embodiment, in the optimal solution search, the first fitness of the currently searched feasible solution is set to P i , the second fitness of the feasible solution found last time is P i-1 , the fitness adjustment factor is C, then the current search step calculation model W is:

[0086] W=C*|P i -P i-1 | (2),

[0087] The search step size is adjusted by changing the size of the adjustment factor. When the fitness value difference is large, the adjustment factor C is increased to improve the convergence speed of the target neural network. When the fitness value is small, the adjustment factor C is reduced to improve the search accuracy of the target neural network.

[0088] In a preferred embodiment, Figure 5 , Figure 5 This is a fifth schematic diagram of the steps of the high positioning accuracy vehicle adaptive navigation method of the present invention; step 200 also includes: step 260, obtaining the satellite navigation signal measurement value of the receiving unit at the current moment; step 270, obtaining the nonlinear measurement function of the satellite navigation signal and its first noise covariance matrix; step 280, calculating and obtaining the second satellite navigation signal based on the satellite navigation signal measurement value, the nonlinear measurement function, and the first noise covariance matrix.

[0089] The satellite navigation signal measurement value at the current time t is z i , the nonlinear measurement function is f(X n ), then the second satellite navigation signal for:

[0090]

[0091] Among them, represents the i-th satellite, Δi is the first noise covariance matrix of the satellite navigation signal.

[0092] In a preferred embodiment, Figure 6 , Figure 6This is a sixth schematic diagram of the steps of the high positioning accuracy vehicle adaptive navigation method of the present invention; step 200 also includes: step 290, using the interference detection model to obtain the deviation value of each satellite; step 291, normalizing the deviation value of each satellite to obtain a weighted parameter matrix.

[0093] When there are multiple satellites, the interference detection model is used to detect the deviation value of each satellite and normalize it. For the i-th satellite, the normalized deviation value is Then the above weight parameter matrix d is:

[0094]

[0095] Where m represents the number of satellites, and the weight parameter matrix d is a diagonal matrix.

[0096] In a preferred embodiment, Figure 7 , Figure 7 This is a seventh schematic diagram of the method for adaptive navigation of a vehicle with high positioning accuracy according to the present invention; step 200 further includes:

[0097] Step 292: Use the weighting parameter matrix to weight the first noise covariance matrix to obtain a second noise covariance matrix; Step 293: Use the second noise covariance matrix to calculate and obtain a third satellite navigation signal.

[0098] The first noise covariance matrix is ​​calculated using the weight parameter matrix d Δi Weighting is helpful to reduce the impact of abnormal signals on the satellite navigation system.

[0099] Step 300: Obtain a fusion filter, use an inertial module to obtain the inertial navigation signal at the current moment, use the fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain a predicted state signal of the vehicle at the current moment.

[0100] In a preferred embodiment, fault detection is performed on the fusion filter to prevent excessive deviation of the predicted state signal output after a sudden change in the fusion filter.

[0101] In this embodiment, the Kalman fusion filter is used for fault detection. First, its residual r is calculated. k :

[0102] r n =Z n -H n X n|n-1 (5),

[0103] Among them, H n is the measurement matrix, X n|n-1 is the predicted state signal.

[0104] The fault detection model of the fusion filter is constructed using formula (5):

[0105]

[0106] in, is the residual matrix r n The inverse matrix of the variance, γ n Obey the chi-square distribution with N degrees of freedom, that is, γ n ~χ 2 (N), N is the dimension of the measurement matrix, is the residual matrix γ n The transposed matrix of .

[0107] By setting the specified threshold, it is determined whether the fusion filter has sent a fault. n When the value is greater than the specified threshold, the fusion filter is deemed faulty and requires correction. By constructing a satellite navigation signal neural network and employing a fitness algorithm to obtain the global optimal solution when the satellite navigation signal loses lock, navigation positioning accuracy is improved. Interference detection models are used to detect interference with navigation satellite signals, and a weighted parameter matrix is ​​used to weight the noise covariance matrix of the satellite navigation signal. This significantly reduces the impact of satellite navigation signal anomalies on navigation system performance and improves navigation accuracy.

[0108] In a second aspect, a vehicle-mounted navigation system 400, such as Figure 8 , Figure 8 The structure diagram of the vehicle navigation system of the present invention is shown in FIG4. It includes an acquisition module 401, a satellite signal module 402, and a fusion module 403. The acquisition module 401 is used to construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain the autocorrelation function of the satellite navigation signal, and use the autocorrelation function to construct an interference detection model. The satellite signal module 402 is used to use a receiving unit to receive the satellite navigation signal at the current moment and make a judgment: if the satellite navigation signal is locked, then use the target satellite navigation signal neural network and the fitness algorithm to search for the optimal solution of the satellite navigation signal at the current moment, and obtain the first satellite navigation signal at the current moment. signal; if the satellite navigation signal is not lost, a second satellite navigation signal at the current moment is obtained, and a signal anomaly detection is performed on the second satellite navigation signal using an interference detection model. If a signal anomaly exists, a weighted parameter matrix corresponding to each satellite is obtained using the interference detection model, and the second satellite navigation signal is weighted using the weighted parameter matrix to obtain a third satellite navigation signal; a fusion module 403 is used to obtain a fusion filter, use an inertial module to obtain the inertial navigation signal at the current moment, and use a fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain a predicted state signal of the vehicle at the current moment.

[0109] Furthermore, the acquisition module 401 includes: an integration unit, used to integrate the received satellite navigation signal with the locally stored pseudocode and carrier signal to obtain the autocorrelation function of the satellite navigation signal; a first calculation unit, used to perform phase tracking on the pseudocode, calculate the leading branch slope and the lagging branch slope of the acquired pseudocode respectively, and perform real part operation and imaginary part operation on the autocorrelation function to obtain the real part function and the imaginary part function respectively; a construction unit, used to obtain an interference detection model using the leading branch slope, the lagging branch slope, the real part function and the imaginary part function.

[0110] Furthermore, the satellite signal module 402 includes: a second calculation unit, which is used to adaptively calculate and obtain a search step size based on the fitness of two adjacent searched solutions; a search unit, which is used to search around the current first initial solution according to the search step size. If the fitness of the searched solution is greater than the fitness of the current first initial solution, the searched solution is used as the current global optimal solution, and a search is performed near each first initial solution to obtain a first global optimal solution set. Each solution in the first global optimal solution set is used as a second initial solution for optimal search until the global optimal solution is found.

[0111] Furthermore, the second calculation unit is further used to: obtain the first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution, obtain the fitness adjustment factor, and use the fitness adjustment factor, the first fitness, and the second fitness to obtain the search step size.

[0112] Simulation Implementation

[0113] A trajectory starting point can be selected, such as M° east longitude and N° north latitude, and the parameters of each sensor of the inertial navigation unit, such as the three-axis acceleration sensor and gyroscope, can be initialized. The performance of the method can be evaluated by comparing the traditional joint navigation and the adaptive navigation model of this application.

[0114] The simulation results are shown in Tables 1 and 2. Table 1 is a comparison table of errors after 30 seconds of loss of lock, and Table 2 is a comparison table of convergence steps of the two models.

[0115] Table 1 Comparison of 30S Errors after Loss of Lock

[0116]

[0117] Table 2 Convergence step length comparison table

[0118]

[0119] It can be seen from Tables 1 and 2 that when the loss of lock time increases, the adaptive navigation method in this application has a more obvious convergence effect on the eastward velocity and position error than the other two methods, while the northward position and velocity errors are improved less, but it has a certain effect on suppressing the lateral position error. The average eastward position error can be reduced to within 3m, and the average northward position error can be reduced to within 2m.

[0120] A high-positioning-precision vehicle adaptive navigation method and a vehicle-mounted navigation device implemented in the present invention improve navigation positioning accuracy by constructing a satellite navigation signal neural network and adopting a fitness algorithm to obtain a global optimal solution when the satellite navigation signal loses lock. Interference to the navigation satellite signal is detected through an interference detection model, and the noise covariance matrix of the satellite navigation signal is weighted using a weighted parameter matrix. This greatly reduces the impact of satellite navigation signal anomalies on navigation system performance and improves navigation accuracy.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high positioning accuracy vehicle adaptive navigation method, characterized in that: include: Step 100: construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain an autocorrelation function of the satellite navigation signal, and use the autocorrelation function to construct an interference detection model; Step 200: Receive the current satellite navigation signal using a receiving unit and make a judgment: If the satellite navigation signal is locked, using the target satellite navigation signal neural network and searching for an optimal solution of the satellite navigation signal at the current moment according to a fitness algorithm to obtain a first satellite navigation signal at the current moment; If the satellite navigation signal is not lost, obtaining a second satellite navigation signal at the current moment, performing signal anomaly detection on the second satellite navigation signal using the interference detection model, and if a signal anomaly exists, obtaining a weighting parameter matrix corresponding to each satellite using the interference detection model, and weighting the second satellite navigation signal using the weighting parameter matrix to obtain a third satellite navigation signal; Step 300: Obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at the current moment, and use the fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain a predicted state signal of the vehicle at the current moment.

2. The high positioning accuracy vehicle adaptive navigation method according to claim 1, characterized in that: The step 100 includes: Step 110: Integrate the received satellite navigation signal with the locally stored pseudo code and carrier signal to obtain the autocorrelation function; Step 120: performing phase tracking on the pseudocode, respectively calculating and obtaining the leading branch slope and the lagging branch slope of the pseudocode, and performing real part operation and imaginary part operation on the autocorrelation function to obtain the real part function and the imaginary part function respectively; Step 130: Obtain the interference detection model using the leading branch slope, the lagging branch slope, the real part function, and the imaginary part function.

3. The high positioning accuracy vehicle adaptive navigation method according to claim 1, characterized in that: The step 200 includes: Step 210: The target satellite navigation signal neural network randomly generates a plurality of first initial solutions; Step 220: adaptively calculate and obtain the search step length based on the fitness of two adjacent searched solutions; Step 230: Searching around the current first initial solution according to the search step size. If the fitness of the searched solution is greater than the fitness of the current first initial solution, the searched solution is used as the current global optimal solution. Searching is performed around each of the first initial solutions to obtain a first global optimal solution set. Step 240: Perform optimal search using each solution in the first global optimal solution set as a second initial solution; Step 250: Repeat steps 210-240 until a global optimal solution is found.

4. The high positioning accuracy vehicle adaptive navigation method according to claim 3, characterized in that: The step 220 includes: Step 221: Obtain the first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution; Step 222: Obtain a fitness adjustment factor, and use the fitness adjustment factor, the first fitness, and the second fitness to obtain the search step length.

5. The high positioning accuracy vehicle adaptive navigation method according to claim 4, characterized in that: After step 250, step 200 further includes: Step 260: Obtain the satellite navigation signal measurement value of the receiving unit at the current moment; Step 270: Obtain a nonlinear measurement function of the satellite navigation signal and a first noise covariance matrix thereof; Step 280: Obtain the second satellite navigation signal by calculation according to the satellite navigation signal measurement value, the nonlinear measurement function, and the first noise covariance matrix.

6. The high positioning accuracy vehicle adaptive navigation method according to claim 5, characterized in that: After step 280, step 200 further includes: Step 290: Obtain the deviation value of each satellite using the interference detection model; Step 291: Normalize the deviation value of each satellite to obtain the weighted parameter matrix.

7. The high positioning accuracy vehicle adaptive navigation method according to claim 6, characterized in that: After step 291, step 200 further includes: Step 292: weighting the first noise covariance matrix using the weighting parameter matrix to obtain a second noise covariance matrix; Step 293: Calculate and obtain the third satellite navigation signal using the second noise covariance matrix.

8. A vehicle-mounted navigation system, using the high positioning accuracy vehicle adaptive navigation method according to any one of claims 1 to 7, characterized in that: include: Get module; Satellite signal module; Fusion module; The acquisition module is used to construct a satellite navigation signal neural network, train the satellite navigation signal neural network to obtain a target satellite navigation signal neural network, obtain an autocorrelation function of the satellite navigation signal, and construct an interference detection model using the autocorrelation function; The satellite signal module is used to receive the current satellite navigation signal using a receiving unit and determine: If the satellite navigation signal is locked, using the target satellite navigation signal neural network and searching for an optimal solution of the satellite navigation signal at the current moment according to a fitness algorithm to obtain a first satellite navigation signal at the current moment; If the satellite navigation signal is not lost, obtaining a second satellite navigation signal at the current moment, performing signal anomaly detection on the second satellite navigation signal using the interference detection model, and if a signal anomaly exists, obtaining a weighting parameter matrix corresponding to each satellite using the interference detection model, and weighting the second satellite navigation signal using the weighting parameter matrix to obtain a third satellite navigation signal; The fusion module is used to obtain a fusion filter, use the inertial module to obtain the inertial navigation signal at the current moment, and use the fusion filter to fuse the first satellite navigation signal or the third satellite navigation signal with the inertial navigation signal to obtain the predicted state signal of the vehicle at the current moment.

9. The vehicle-mounted navigation system according to claim 8, characterized in that: The acquisition module includes: an integration unit, configured to integrate the received satellite navigation signal with the locally stored pseudo code and carrier signal to obtain an autocorrelation function of the satellite navigation signal; a first calculation unit, configured to perform phase tracking on the pseudocode, respectively calculate and obtain the leading branch slope and the lagging branch slope of the pseudocode, and perform a real part operation and an imaginary part operation on the autocorrelation function to obtain a real part function and an imaginary part function, respectively; A construction unit is used to obtain the interference detection model using the leading branch slope, the lagging branch slope, the real part function and the imaginary part function.

10. The vehicle-mounted navigation system according to claim 9, characterized in that: The satellite signal module includes: The second calculation unit is used to adaptively calculate and obtain the search step length according to the fitness of two adjacent searched solutions; A search unit is used to search around the current first initial solution according to the search step size. If the fitness of the searched solution is greater than the fitness of the current first initial solution, the searched solution is used as the current global optimal solution, and a search is performed near each of the first initial solutions to obtain a first global optimal solution set. Each solution in the first global optimal solution set is used as a second initial solution for optimal search until the global optimal solution is found.

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