High-positioning-precision vehicle adaptive navigation method and vehicle-mounted navigator

Abstract

The invention discloses a high-positioning-precision vehicle adaptive navigation method and a vehicle-mounted navigator. The method comprises the following steps: constructing a satellite navigation signal neural network and an interference detection model; receiving the satellite navigation signal at the current moment by using a receiving unit, searching and acquiring an optimal solution of the satellite navigation signal at the current moment by using a target satellite navigation signal neural network according to a fitness algorithm, or weighting a second satellite navigation signal by using a weighting parameter matrix; and fusing the satellite navigation signal and the inertial navigation signal by using a fusion filter to obtain a predicted state signal of the vehicle at the current moment. The interference detection model is used for detecting the interference suffered by the navigation satellite signal, and the weighting parameter matrix is used for weighting the noise covariance matrix of the satellite navigation signal, so that the influence of the abnormal satellite navigation signal on the performance of the navigation system is greatly weakened, and the navigation precision is improved.

Description

Technical Field

[0001] The 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] As an emerging industry, in-vehicle navigation services are gaining wide attention, and with the increasing demand of users, they are showing broad application prospects. At present, the mainstream in-vehicle navigation terminals on the market mainly provide humanized driving route planning functions, and have extended functions such as real-person voice prompts. The main functions of the in-vehicle navigation terminal are: key selection whether to update the increment, positioning function, real-time download of remote electronic maps, real-time display of positioning information and map data and navigation, the main control chip can enter the power-down mode to reduce power consumption, and the main control chip can be immediately awakened by pressing the corresponding function key. In-vehicle navigation regularly updates the map data, that is, the service provider updates the map data at regular intervals, and users regularly download maps from the service provider for update.

[0003] In real-time navigation, the landing signal power of the global satellite navigation system in the vehicle-mounted navigation terminal is low and is susceptible to various interferences, resulting in poor availability. Especially in complex urban environments, there are multipath interference, satellite signals are blocked or satellites are invisible, 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 in the combined filtering effect or unusable results, thereby causing the positioning accuracy of the navigator to decrease. Summary of the invention

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

[0005] In view of the above problems, a high positioning accuracy vehicle adaptive navigation method and a vehicle-mounted navigator 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 is lost, 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, which greatly reduces the impact of satellite navigation signal anomalies on the performance of the navigation system and improves the 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 construct an interference detection model using the autocorrelation function;

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

[0009] If the satellite navigation signal is unlocked, the target satellite navigation signal neural network is used to search and obtain the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm to obtain the first satellite navigation signal at the current moment;

[0010] If the satellite navigation signal is not locked, 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 the 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;

[0011] Step 300: obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at a 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.

[0012] In conjunction with the high positioning accuracy vehicle adaptive navigation method described in the first aspect of the present invention, in a first possible implementation manner, 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 function and the imaginary 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 manner, 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 according to the fitness of two adjacent searched solutions;

[0019] Step 230: 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.

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

[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, obtaining 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: obtaining 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 according to the satellite navigation signal measurement value, the nonlinear measurement function, and the first noise covariance matrix calculation.

[0029] In combination 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 combination 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 acquire the third satellite navigation signal.

[0035] In a second aspect, a vehicle-mounted navigation device 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 satellite navigation signal at the current moment using the receiving unit and make a judgment:

[0041] If the satellite navigation signal is unlocked, the target satellite navigation signal neural network is used to search and obtain the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm to obtain the first satellite navigation signal at the current moment;

[0042] If the satellite navigation signal is not locked, 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 the 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;

[0043] The fusion module is used to obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at a 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.

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

[0045] An integration unit, used 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 is used to perform phase tracking on the pseudo code, respectively calculate and obtain the leading branch slope and the lagging branch slope of the pseudo code, 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 combination 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] A second calculation unit is used to adaptively calculate and obtain a search step length according to the fitness of two consecutive solutions searched;

[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] The first fitness of the currently searched feasible solution and the second fitness of the last searched feasible solution are obtained, and a fitness adjustment factor is obtained. The search step length is obtained using the fitness adjustment factor, the first fitness, and the second fitness.

[0053] The high positioning accuracy vehicle adaptive navigation method and vehicle-mounted navigator described in the present invention are implemented to improve the 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 by a weighted parameter matrix, which greatly reduces the impact of satellite navigation signal anomalies on the performance of the navigation system and improves the 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 first schematic diagram of the steps of the high positioning accuracy vehicle adaptive navigation method of the present invention;

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

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

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

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

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

[0061] 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;

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

[0063] The following will be combined with the accompanying drawings in the invention to clearly and completely describe the technical solutions in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in 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-mounted navigation systems decreases when the satellite is blocked and cannot be seen or is subject to multipath interference.

[0065] In view of 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 the autocorrelation function; Step 120, perform phase tracking on the pseudocode, calculate and obtain the leading branch slope and the lagging branch slope of the pseudocode, 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; Step 130, use the leading branch slope, the lagging branch slope, the real part function and the imaginary part function to obtain the interference detection model.

[0076] In this embodiment, it is assumed that a certain 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 for time T, and the noise term is ignored to obtain the autocorrelation function x(ε) of the above-mentioned 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 pseudo code phase error is estimated using the output result of the phase detector.

[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 leading branch slope and the delayed branch slope of the loop are calculated, and the phase detector factor μ is obtained from the leading branch slope and the delayed branch slope. From the above, the interference detection model D can be obtained as follows:

[0079] D=|μ*(K1 / K2)| (1),

[0080] The detection principle of satellite navigation signals using the above 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 near 0. If there is multipath interference and the loop is unstable in tracking the pseudo code phase, the phase detector factor μ is not zero, the real part K2 is not much different from the imaginary part K1, and the D value is not zero. The stronger the interference signal, the larger the D value.

[0081] Step 200, using a receiving unit to receive the satellite navigation signal at the current moment, and make a judgment: if the satellite navigation signal is locked, use the target satellite navigation signal neural network and search for the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm 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, 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 a third satellite navigation signal.

[0082] In this embodiment, the satellite navigation is firstly checked to see if it has lost lock. If it has lost lock, the trained neural network of the target satellite navigation signal 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 accuracy 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 according to 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, 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 the 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 accuracy 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 searched 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 length 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 according to 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] Among them, 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 also includes:

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

[0098] The first noise covariance matrix is ​​calculated by 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 an inertial navigation signal at the current moment, 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.

[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 It obeys the chi-square distribution with N degrees of freedom, that is, γ n ~x 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 sends a fault. n When it is greater than the specified threshold, the fusion filter is judged to be faulty and needs to be corrected. 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 detection model is used to detect the interference to the navigation satellite signal and the noise covariance matrix of the satellite navigation signal is weighted using a weighted parameter matrix, which greatly reduces the impact of satellite navigation signal anomalies on the performance of the navigation system and improves navigation accuracy.

[0108] In a second aspect, a vehicle-mounted navigation device 400, such as Figure 8 , Figure 8 The structure diagram of the vehicle navigation system of the present invention is shown in FIG4. The vehicle navigation system 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 construct an interference detection model using the autocorrelation function. The satellite signal module 402 is used to use a receiving unit to receive the satellite navigation signal at the current moment, and to make a judgment: if the satellite navigation signal is unlocked, then use the target satellite navigation signal neural network and search for the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm, 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, an inertial module is used to obtain the inertial navigation signal at the current moment, and a fusion filter is used 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, which is 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, which is 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, which 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.

[0110] Furthermore, the satellite signal module 402 includes: a second calculation unit, which is used to adaptively calculate and obtain a search step length according to the fitness of solutions searched twice adjacently; a search unit, which is used to search around the current first initial solution according to the search step length. 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, and 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 length.

[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 a three-way acceleration sensor and a gyroscope, can be initialized. The performance of the method can be evaluated by comparing 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 when the system is locked for 30 seconds, and Table 2 is a comparison table of convergence steps of the two models.

[0115] Table 1. Comparison of 30S error after losing 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 the present application has a more obvious convergence effect on the eastward speed and position error than the other two methods, while the northward position and speed 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 accuracy vehicle adaptive navigation method and a vehicle-mounted navigator implementing the present invention improve the 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, and detects interference to the navigation satellite signal through an interference detection model and uses a weighted parameter matrix to weight the noise covariance matrix of the satellite navigation signal, which greatly reduces the impact of satellite navigation signal anomalies on the performance of the navigation system 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 substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope 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 construct an interference detection model using the autocorrelation function; Step 200: Receive the current satellite navigation signal using a receiving unit and make a judgment: If the satellite navigation signal is unlocked, the target satellite navigation signal neural network is used to search and obtain the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm to obtain the first satellite navigation signal at the current moment; 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 the 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; Step 300: obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at a 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.

2. The high positioning accuracy vehicle adaptive navigation method according to claim 1, characterized in that: The step 100 comprises: 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 comprises: 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 according to the fitness of two adjacent searched solutions; Step 230: 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. Step 240: Use each solution in the first global optimal solution set as a second initial solution to perform optimal search; 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, obtaining 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: obtaining 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: Utilize the second noise covariance matrix to calculate and acquire the third satellite navigation signal.

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 satellite navigation signal at the current moment using the receiving unit and make a judgment: If the satellite navigation signal is unlocked, the target satellite navigation signal neural network is used to search and obtain the optimal solution of the satellite navigation signal at the current moment according to the fitness algorithm to obtain the first satellite navigation signal at the current moment; 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 the 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; The fusion module is used to obtain a fusion filter, use an inertial module to obtain an inertial navigation signal at a 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.

9. The vehicle-mounted navigation system according to claim 8, characterized in that: The acquisition module comprises: An integration unit, used 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 is used to perform phase tracking on the pseudo code, respectively calculate and obtain the leading branch slope and the lagging branch slope of the pseudo code, 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 device according to claim 9, characterized in that: The satellite signal module comprises: A second calculation unit is used to adaptively calculate and obtain a 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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