A vehicle GNSS positioning method based on multi-motion model interaction

Abstract

This invention discloses a vehicle GNSS positioning method based on the interaction of multiple motion models. The method includes establishing a PCV model and a PCSAV model for a vehicle based on its two postures, namely, straight-line driving and turning driving, to obtain the vehicle's state estimate vector and state transition matrix based on the PCV and PCSAV models at the previous moment. The method then introduces an interactive multi-model approach to establish a heuristic position-velocity filtering HPV-IMM model based on the interactive multi-model, enabling information filtering interaction between the PCV and PCSAV models to obtain the vehicle's current state estimate vector and error covariance matrix, thereby determining the vehicle's current position and velocity. This method addresses the low accuracy of traditional single kinematic models in multi-motion vehicle positioning and is suitable for dynamic navigation of multi-posture vehicle motion in urban environments.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle navigation and positioning, and in particular to a vehicle GNSS positioning method based on multi-motion model interaction. Background Art

[0002] In an open, interference-free environment, the SPP positioning accuracy of ordinary vehicle-mounted navigation receivers can reach the meter level, and the RTD positioning accuracy can reach the sub-meter level. However, the large number of urban canyons, tree-lined areas, tunnels, overpasses, and other scenes in cities can easily cause obstruction and deception of satellite signals. Among them, severe non-line-of-sight (NLOS) and multipath effects make it impossible to guarantee the availability and continuity of SPP / RTD positioning. In extreme environments, positioning errors may reach tens of meters or even hundreds of meters. Faced with the above difficulties, in order to improve the robustness and anti-interference ability of the navigation system in the face of abnormal observation data, there are roughly two types of solutions without relying on external sensor information assistance:

[0003] First, in the observation domain, representative methods include fault detection and exclusion (FDE) and robust estimation. The core goal of the former is to identify and exclude observations with significant bias by performing a series of statistical tests, thereby ensuring the accuracy and stability of the system output. The latter, on the other hand, focuses on optimizing the matching of observations with their weights to mitigate the adverse impact of observations with large errors and high weights on the overall filtering results. Both methods can be implemented on prior information and posterior residuals. FDE and robust estimation based on posterior residuals comprehensively consider prior innovations and observation data, better reflecting the distribution of observation residuals at the current moment. However, due to the correlation between observations, some gross errors are assigned to other normal observations, which can easily lead to missed detections and false alarms. While FDE and robust estimation based on prior innovations can avoid the negative impact of gross error transfer, they impose stricter requirements on the accuracy of state forecasts.

[0004] Second, in the state domain, most navigation receivers on the market can currently receive Doppler observation signals, and GNSS multi-system single-point velocity measurement can achieve an accuracy of cm / s. Considering that the vehicle's motion state is relatively stable, using the carrier's prior coordinates and Doppler velocity information to predict the current coordinates is more robust than the traditional single-point positioning coordinate update method. In the past 20 years, many scholars have discussed the issue of carrier motion models and established various models, including the constant velocity (CV) model and the constant acceleration (CA) model, to describe the carrier's motion behavior. Among them, the CV model is the most widely used, but it is too ideal and difficult to accurately describe the complex motion state of vehicles driving in cities, especially when the vehicle is in a large curvature turn state. The prediction effect is poor. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a vehicle GNSS positioning method based on multi-motion model interaction, which can accurately locate the multi-attitude motion state of the vehicle.

[0006] Technical Solution: To achieve the above objectives, the present invention provides a vehicle GNSS positioning method based on multi-motion model interaction, comprising:

[0007] A heuristic position-constant velocity PCV model and a position-constant angular velocity PCSAV model of the carrier are established according to the two postures of the carrier in straight-line driving and turning driving, respectively, to obtain the state estimation vector and state transfer matrix of the carrier at the previous moment based on the PCV model and the PCSAV model. The state estimation vector includes the position and velocity of the carrier, and the state transfer matrix is used to transfer the state vector at the previous moment to the next moment;

[0008] An interactive multi-model is introduced, and a heuristic position-velocity filtering HPV-IMM model based on the interactive multi-model is established. The HPV-IMM model predicts and measures the state estimation vector and error covariance matrix of the carrier at the current moment based on the PCV model and PCSAV model according to the state estimation vector and state transfer matrix of the PCV model and PCSAV model at the previous moment, and obtains the carrier state vector and error covariance matrix of the PCV model and PCSAV model at the current moment after measurement update; the carrier state vector and error covariance matrix of the PCV model and PCSAV model at the current moment after measurement update are fused to obtain the state estimation vector and error covariance matrix of the carrier at the current moment, and the position and velocity of the carrier at the current moment are obtained based on the state estimation vector of the carrier at the current moment.

[0009] In the PCV model, the state vector of the carrier to be estimated is expressed as:

[0010] XPCV =[x,y,z,V x ,V y ,V z ];

[0011] The state transfer matrix of the carrier is expressed as:

[0012]

[0013] Where dt represents the time interval between adjacent epochs; (x, y, z) represents the position of the carrier at a certain moment, (V x ,V y ,V z ) represents the velocity of the carrier at position (x, y, z); the state transfer matrix transfers the state vector of the previous moment to the next moment according to the time interval dt.

[0014] Among them, in the PCSAV model, the state vector of the carrier to be estimated can be expressed as:

[0015] X PCSAV =X PCV ,

[0016] Where, X PCV Represents the state vector to be estimated for the carrier in the PCV model;

[0017] The carrier state transfer matrix is expressed as:

[0018]

[0019] Where ω(T) represents the carrier angular velocity at time T; H represents the transformation matrix from the ECEF system to the ENU system.

[0020] The interactive multi-HPV-IMM model includes a state interactive input module, a state prediction module, an innovation outlier detection module, a measurement update module, and an overall output interactive module.

[0021] Among them, the state interaction input module calculates the initial estimated state vector of the i sub-model carrier at the current k+1 moment based on the estimated result of the state vector of the i sub-model carrier at the previous moment k and its error covariance matrix Wherein, when i=1, the i sub-model represents the PCV model, when i=2 or M, the i sub-model represents the PCSAV model, and M=2 represents the number of sub-models;

[0022] The state prediction module is based on the initialization state vector and the error covariance matrix Predict the motion state of the i-sub-model carrier from time k to time k+1, and obtain the state prediction vector of the i-sub-model carrier from time k to time k+1 and the error covariance matrix

[0023] The innovation outlier detection module predicts the vector according to the state of the i-submodel carrier. and the error covariance matrix Calculate the new information sequence of sub-model i at time k+1

[0024] The measurement update module includes measurement update and sub-model probability update. The measurement update is based on the state prediction vector of the i sub-model carrier. Error covariance matrix Innovation sequence Perform measurement update to obtain the state vector after measurement update of the i-submodel carrier and the error covariance matrix The sub-model probability update is achieved by calculating the similarity between the i-sub-model and the current carrier motion state, and obtaining the posterior probability of the i-sub-model.

[0025] The overall interactive output module converts the state estimation vector of the i sub-model carrier into and the error covariance matrix According to the posterior probability Perform weighted fusion to obtain the weighted fusion and the error covariance matrix That is, the state estimation vector and error covariance matrix of the carrier at time k+1;

[0026] The position and velocity of the carrier at time k+1 are obtained based on the state estimation vector of the carrier at time k+1.

[0027] Among them, the i sub-model carrier initializes the state vector and the error covariance matrix Expressed as:

[0028]

[0029]

[0030] Where, Represents the parameters of the i-th sub-model at time k, including the error covariance matrix State transition matrix Process noise matrix Coefficient matrix Observation noise matrix Represents the observation data at time k, including the pseudorange value of the satellite and Doppler measurements

[0031] Indicated by arrive The transition probability of Represents the probability of the sub-model after the interaction in the input interaction module, through the prior transfer probability matrix π and the model posterior probability Calculated, the transition probability matrix is set to

[0032] Among them, the state prediction vector of the i-sub-model carrier from time k to time k+1 is and the error covariance matrix Expressed as:

[0033]

[0034] Where, Describe the state transfer matrix of the i-submodel carrier at time k+1, represents the process noise matrix of sub-model i at time k+1.

[0035] Among them, the new information sequence of sub-model i at time k+1 is Expressed as:

[0036]

[0037] Where, It represents the observation data after eliminating gross errors at time k+1. Represents the coefficient matrix of sub-model i at time k+1.

[0038] Among them, a multi-dimensional statistical analysis outlier detection algorithm is used to identify and eliminate the observation value gross errors in the observation values, and the observation value gross errors refer to the observation values in the observation values whose errors are greater than a set threshold.

[0039] Among them, the i sub-model carrier measures the updated state vector and the error covariance matrix Expressed as:

[0040]

[0041] Where I represents the identity matrix (the main diagonal elements are 1), represents the coefficient matrix of sub-model i at time k+1; represents the Kalman gain

[0042] The posterior probability of the i submodel Expressed as:

[0043]

[0044] Where, Represents the likelihood function value of sub-model i at time k+1.

[0045] Among them, the weighted fusion and the error covariance matrix That is, the state estimation vector and error covariance matrix of the carrier at time k+1 are expressed as:

[0046]

[0047] Beneficial effects: The present invention has the following advantages: The interactive multi-model proposed in the present invention fully considers the importance of the kinematic model for GNSS vehicle navigation and positioning, and based on the principle of full probability, realizes real-time interaction of multiple vehicle kinematic models through the interactive multi-model, solves the problem of low accuracy of the traditional single kinematic model in multi-motion posture vehicle positioning, and is suitable for dynamic navigation of multi-posture vehicle motion in urban environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a heuristic PCV model;

[0049] Figure 2 It is a heuristic PCSAV model;

[0050] Figure 3 Schematic diagram of the flow of information filtering interaction between PCV model and PCSAV model for interactive multi-model implementation;

[0051] Figure 4 Schematic diagram of interactive multi-model to achieve real-time interaction between PCV model and PCSAV model in open scene;

[0052] Figure 5 Schematic diagram comparing the positioning trajectories of the HPV-IMM model and the existing technology;

[0053] Figure 6 These are the results of HPV-IMM model positioning in a complex environment before and after applying the MSA-OD algorithm. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described in detail below with reference to the embodiments and drawings.

[0055] The present invention provides a vehicle GNSS positioning method based on multi-motion model interaction, comprising the following steps:

[0056] Step 1: Establish a heuristic PCV model (position-constant velocity model) and a PCSAV model (position-constant angular velocity model) based on the vehicle's straight-line driving and turning driving postures respectively;

[0057] Heuristic PCV models such as Figure 1 As shown, when the carrier (vehicle) is in a straight-line driving state, it can be constrained to the PCV model, that is, it is assumed that the carrier's movement speed at time T3 is equal in magnitude and unchanged in direction compared to time T2, and the carrier's position at time T3 is predicted by the instantaneous speed at time T2.

[0058] In the PCV model, the clock error and clock rate of the vehicle receiver are eliminated through inter-satellite difference. The state vector to be estimated by the carrier can be expressed as:

[0059] X PCV =[x,y,z,V x ,V y ,V z ] (1);

[0060] The state transfer matrix of the carrier is:

[0061]

[0062] Where dt represents the time interval between adjacent epochs; (x, y, z) represents the position of the carrier at a certain moment, (V x ,V y ,V z ) represents the velocity of the carrier at position (x, y, z); the state vector usually contains the position and velocity of the carrier at a certain moment, and the state transfer matrix transfers the state vector of the previous moment to the next moment according to the time interval dt.

[0063] The heuristic PCSAV model is as follows Figure 2 As shown in the figure, when the carrier is in a turning state, it is constrained to the PCSAV model, that is, it is assumed that the speed of the carrier at time T3 remains unchanged compared to time T2, and the direction is corrected by the angular velocity of the carrier at time T2, and the position of the carrier at time T3 is predicted by the average speed of the carrier from time T2 to time T3.

[0064] In the PCSAV model, the state vector of the carrier to be estimated can be expressed as:

[0065] X PCSAV =X PCV (3);

[0066] The carrier state transfer matrix can be expressed as

[0067]

[0068] Where:

[0069]

[0070] The angular velocity of the carrier at time T, that is, the rate of change of the carrier's heading, can be derived from the heading angle θ at the current and previous moments, and the heading angle θ can be derived from the carrier's eastward and northward speeds (V e ,V n ) calculated.

[0071]

[0072] represents the conversion matrix from ECEF system to ENU system, B and L are the initialization state vectors of PCV or PCSAV model respectively. The latitude and longitude of the medium carrier.

[0073]

[0074] Indicates that the horizontal component of the carrier velocity rotates around the origin The rotation matrix of degrees.

[0075] The heuristic PCV and PCSAV models developed in this paper do not rely on data input from external sensors. All parameter calculations for these two models rely solely on data from the current and previous moments, reducing computational complexity. Compared to methods that require motion constraints through sliding window fitting of trajectories, this significantly improves the real-time performance of the vehicle position calculation process.

[0076] Step 2: If Figure 3 As shown, an interactive multi-model (HPV-IMM model) is established to realize information filtering interaction between the PCV model and the PCSAV model;

[0077] The HPV-IMM model consists of five modules: state interaction input module, state prediction module, innovation outlier detection module, measurement update module (including measurement update and sub-model probability update), and overall output interaction module.

[0078] (1) The state interaction input module calculates the initial estimated state of the sub-model carrier at the current k+1 time based on the state estimation of the sub-model (PCV model, PCSAV model) at the previous moment (referring to the estimated result of the sub-model state vector), the sub-model posterior probability, and the transition probability matrix π. and its error covariance Among them, i=1 represents the PCV model, i=2 or M represents the PCSAV model, and M=2 represents the number of sub-models.

[0079] Unlike the extended Kalman filter (EKF) of a single model, in the state prediction process, the interactive multi-model does not directly use the state estimate and error covariance matrix of the previous epoch for the state prediction of the current epoch. Instead, the sub-models are initialized at the beginning of each epoch and interactively fused in the state domain.

[0080] According to the total probability theorem, the probability density function (PDF) of the state estimate of each sub-model at time k+1 can be initialized as:

[0081]

[0082] The initialization state vector of each sub-model at time k+1 and its error covariance are expressed as:

[0083]

[0084] Where,

[0085]

[0086] Represents the relevant parameters of the i-submodel at time k, including: error covariance matrix State transition matrix Process noise matrix Coefficient matrix Observation noise matrix

[0087]

[0088] represents the observation data at time k, which in this embodiment is specifically the pseudorange and Doppler measurement values of the satellite.

[0089]

[0090] Indicated by arrive The transition probability can be calculated by the prior transition probability matrix (TransitionProbability Matrix, TPM) π and the posterior probability of the sub-model.

[0091] The superscript "-" of the above variables indicates the prediction result (prior), "~" indicates the interaction result, and "^" indicates the estimated result after the measurement update (posterior). The posterior probability of the sub-model refers to the probability after the probability update. The transition probability matrix is The subscript k / k above represents the variable at time k, k-1 / k-1 represents the variable at time k-1, and k+1 / k represents the prediction from the variable at time k to time k+1.

[0092] (2) The state prediction module is independent in each sub-model filter (i.e., each sub-model filter has a state prediction module). The initial state vector obtained by the state interaction input module and the error covariance matrix On this basis, the motion state of the sub-model carrier from time k to time k+1 is predicted:

[0093]

[0094] Where, Represents the state prediction vector and error covariance matrix of the i sub-model carrier from time k to time k+1 Describe the state transfer matrix of the sub-model carrier at time k+1.

[0095] (3) Outlier detection module, based on the state prediction vector obtained by the state prediction module Based on this, calculate the new information sequence of the sub-model filter at time k+1

[0096]

[0097] Where, It represents the observation data after eliminating gross errors at time k+1. Represents the coefficient matrix of sub-model i at time k+1.

[0098] in,

[0099]

[0100] Where, represents the pseudorange innovation sequence, represents the Doppler innovation sequence.

[0101] When performing pseudorange single-point positioning and Doppler single-point velocity measurement, the original pseudorange and Doppler observation equations can be expressed as:

[0102]

[0103] Ignore satellite clock error cdt s , ionospheric delay d ion and the tropospheric delay d trop The modeling error is known from the above observation equation, the pseudorange innovation sequence Mainly includes receiver clock difference cdt r , satellite-to-ground range ρ deviation caused by forecast position deviation and pseudorange observation noise ε PR . Same pseudorange innovation sequence, Doppler innovation sequence Mainly determined by the receiver clock speed Satellite-to-ground distance change rate caused by predicted velocity deviation Bias and Doppler observation noise ε DOP composition.

[0104] Assuming the predicted position and velocity errors are within acceptable limits, pseudorange and Doppler innovation sequences can be considered biased but generally stable data sequences. Sequence outliers caused by gross errors in observations can be effectively identified and eliminated using the MSA-OD algorithm. It is worth noting that during outlier identification, the presence of inter-system bias (ISB) between satellite systems, such as GPS and BeiDou, requires independent analysis of pseudorange innovation sequences from different systems. In contrast, analysis of Doppler sequences does not require this specific consideration.

[0105] (4) The model measurement update module is included in each independent filter, based on the state prediction vector and its error covariance matrix Innovation sequence The measurement is updated through EKF to obtain the state vector after measurement update and its error covariance

[0106] Calculate the Kalman gain:

[0107]

[0108] Update status:

[0109]

[0110] Update error covariance:

[0111]

[0112] Where I represents the identity matrix (the main diagonal elements are 1).

[0113] The posterior probability of the submodel is updated by the current epoch and the model likelihood function value. That is, by calculating the similarity between the submodel and the current carrier motion state, the probability that best suits the current submodel is obtained. The pseudorange and Doppler observations theoretically conform to the normal distribution, and their likelihood function values can be expressed as follows:

[0114]

[0115] Among them, det() means calculating the determinant of the square matrix;

[0116]

[0117] is the innovation error covariance calculated using the error propagation law.

[0118] Therefore, according to Bayes' theorem, the submodel posterior probability can be expressed as:

[0119]

[0120] (5) The overall interactive output module is used to convert each sub-model state estimation vector and the error covariance matrix According to the posterior probability Perform weighted fusion to obtain the final total output of the HPV-IMM model.

[0121]

[0122] According to formula (25), the position and velocity of the carrier at time k+1 are obtained, where the error covariance matrix measures the error of the state vector.

[0123] In the above process, in view of the impact of non-Gaussian distribution observations on the update accuracy of the measurement update module in the urban canyon scenario, a multi-dimensional statistical analysis outlier detection algorithm is used to identify and eliminate the gross errors of the observations, thereby improving the accuracy of the probability of real-time update of the sub-model. The gross errors of the observations refer to the observations with larger errors, and the observations correspond to Equation 12.

[0124] The process of multi-dimensional statistical analysis outlier detection algorithm to identify and eliminate gross errors in observation values is as follows:

[0125] (1) Input the N-dimensional pseudorange or Doppler innovation vector L, the test threshold K0 for comparing the maximum and minimum values of the sequence with the mean, the test threshold K1 for comparing the maximum and minimum values of the sequence with the median, and the test threshold K2 for comparing the maximum and minimum values of the difference sequence;

[0126] (2) Initialize the N-dimensional outlier indicator sequence (0: healthy, 1: abnormal), set the list flag = [00...0] T , test cycle number k = 0,

[0127] (3) If Nk<3, end; otherwise, remove the data marked with gross errors in the L sequence;

[0128] (4) Calculation of inspection quantity:

[0129] Calculate the maximum value element L in L max , minimum element L min and its position i, j in the sequence;

[0130] Calculate the mean Mu and median Med of the L sequence;

[0131] Calculate the maximum value element D in the L difference sequencemax and the minimum value element D min ;

[0132] (5) Outlier detection: If

[0133] L max -L min -2Mu < K0 && L max -Med < K1 && Med - L min < K1 && D max -D min < K2, that is, there are no outliers, end;

[0134] (6) Outlier marking. If L max -L min -2Mu ≥ K0 || L max -Med ≥ K1 || D max -D min ≥ K2, then flag(i) = 1;

[0135] If L max -L min -2Mu ≤ -K0 || L max -Med ≤ -K1 || D max -D min ≤ -K2, then flag(j) = 1;

[0136] (7) Update k to the new k + 1 and re - execute (3).

[0137] As Figure 4 shown, the HPV - IMM model proposed by the present invention can successfully achieve accurate real - time interaction of two sub - models in an open scene. The red line represents the probability of the PCV model, and the green line represents the probability of the PCSAV model.

[0138] As Figure 5 shown, the positioning trajectory of the HPV - IMM is smoother and the positioning accuracy is higher than that of the RTD model in terms of positioning. In terms of speed measurement, it solves the problem that the SPV is prone to "jumping points". Compared with the traditional PV model, the HPV - IMM model is approximately equivalent to the PV model when the vehicle is in a straight - line driving section, and the positioning and speed - measurement accuracies are comparable. When the vehicle is in a turning driving section, due to the real - time probability update mechanism of the HPV - IMM model, the PCSAV model that is more in line with the current vehicle motion behavior has a greater weight, and the positioning and speed - measurement accuracies are better than those of the PV model. The positioning accuracy can be increased by 27.2%, 50%, and 4.9% in the E, N, and U directions respectively compared with the PV model. The speed - measurement accuracy can be increased by 30%, 40%, and 9.0% respectively. In addition, as Figure 6 shown, after removing the gross error of the observed values by the MSA - OD algorithm, the robustness of the HPV - IMM model is effectively improved.

Claims

1. A vehicle GNSS positioning method based on multi-motion model interaction, characterized in that: include: A heuristic position-constant velocity PCV model and a position-constant angular velocity PCSAV model of the carrier are established according to the two postures of the carrier in straight-line driving and turning driving, respectively, to obtain the state estimation vector and state transfer matrix of the carrier at the previous moment based on the PCV model and the PCSAV model. The state estimation vector includes the position and velocity of the carrier, and the state transfer matrix is used to transfer the state vector at the previous moment to the next moment; An interactive multi-model is introduced, and a heuristic position-velocity filtering HPV-IMM model based on the interactive multi-model is established. The HPV-IMM model predicts and measures the state estimation vector and error covariance matrix of the carrier at the current moment based on the PCV model and PCSAV model according to the state estimation vector and state transfer matrix of the PCV model and PCSAV model at the previous moment, and obtains the state vector and error covariance matrix of the carrier of the PCV model and PCSAV model at the current moment after the measurement update; the state vector and error covariance matrix of the carrier of the PCV model and PCSAV model at the current moment after the measurement update are fused to obtain the state estimation vector and error covariance matrix of the carrier at the current moment, and the position and velocity of the carrier at the current moment are obtained based on the state estimation vector of the carrier at the current moment; In the PCV model, the state vector of the carrier to be estimated is expressed as: ； The state transfer matrix of the carrier is expressed as: ； Where, represents the time interval between adjacent epochs; (x, y, z) represents the position of the carrier at a certain moment, (V x ,V y, V z ) represents the velocity of the carrier at position (x, y, z); the state transfer matrix is based on the time interval Transfer the state vector from the previous moment to the next moment; Among them, in the PCSAV model, the state vector to be estimated by the carrier is expressed as: ， Where, Represents the state vector to be estimated for the carrier in the PCV model; The carrier state transfer matrix is expressed as: ， Where, represents the carrier angular velocity; H represents the conversion matrix from the ECEF system to the ENU system, and They are all 3*3 square matrices.

2. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 1, characterized in that: The HPV-IMM model includes a state interaction input module, a state prediction module, an innovation outlier detection module, a measurement update module, and an overall output interaction module; Among them, the state interaction input module is based on The sub-model carrier at the last moment The estimated result of the state vector is calculated Sub-model carrier in the current The initial estimated state vector at time and its error covariance matrix ,in, =1, The sub-model represents the PCV model, =2, The sub-model represents the PCSAV model; The state prediction module is based on the initialization state vector and the error covariance matrix ,right The sub-model carrier predicts the motion state from time k to time k+1, and obtains The state prediction vector of the sub-model carrier from time k to time k+1 and the error covariance matrix ; The innovation outlier detection module is based on Sub-model carrier state prediction vector and the error covariance matrix , calculate the k+1 moment Submodel innovation sequence ; The measurement update module includes measurement update and sub-model probability update. Sub-model carrier state prediction vector , error covariance matrix 、Innovation sequence Perform measurement update and get The sub-model carrier measures the updated state vector and the error covariance matrix ; The sub-model probability is updated by calculating The similarity between the sub-model and the current carrier motion state is obtained Submodel posterior probability ; The overall interactive output module will The state estimation vector of the sub-model carrier and the error covariance matrix , according to the posterior probability Perform weighted fusion to obtain the weighted fusion and the error covariance matrix , that is, the state estimation vector and error covariance matrix of the carrier at time k+1; The position and velocity of the carrier at time k+1 are obtained based on the state estimation vector of the carrier at time k+1.

3. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 2, characterized in that: The probability density function of the state estimate of each sub-model at time is initialized as: ， described Submodel carrier initializes state vector and the error covariance matrix Expressed as: ， ； Where, M=2; express time Submodel parameters, including the error covariance matrix , state transfer matrix , process noise matrix , coefficient matrix , observation noise matrix ; ,express Time observation data, including satellite pseudorange values and Doppler measurements ; , indicating that arrive The transition probability of , represents the probability of the sub-model after interaction in the input interaction module, through the prior transfer probability matrix and the model posterior probability Calculated, the transition probability matrix is set to .

4. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 2, characterized in that: described The state prediction vector of the sub-model carrier from time k to time k+1 and the error covariance matrix Expressed as: ， ； Where, Expressing the k+1 moment The state transition matrix of the sub-model carrier, represents the k+1 moment The process noise matrix of the submodel.

5. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 2, characterized in that: The k+1 moment Submodel innovation sequence Expressed as: ； Where, express The observation data after removing gross errors at all times, represents the k+1 moment Submodel coefficient matrix.

6. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 5, characterized in that: A multi-dimensional statistical analysis outlier detection algorithm is used to identify and eliminate observation value gross errors in the observation values, where the observation value gross errors refer to observation values in the observation values whose errors are greater than a set threshold.

7. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 2, characterized in that: described The sub-model carrier measures the updated state vector and the error covariance matrix Expressed as: ， ； Where I represents the identity matrix, represents the k+1 moment Sub-model coefficient matrix; represents the Kalman gain ; described Submodel posterior probability Expressed as: ； Where, express The likelihood function value of the sub-model at time k+1, Represents the probability of the sub-model after the interaction in the input interaction module.

8. The vehicle GNSS positioning method based on multi-motion model interaction according to claim 2, characterized in that: The weighted fusion and the error covariance matrix , that is, the state estimation vector and error covariance matrix of the carrier at time k+1 are expressed as: ， ， Where M=2.

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