A vehicle-mounted dual-antenna tightly coupled positioning method and system in complex environments

Abstract

The present invention belongs to the field of joint navigation technology and provides a vehicle-mounted dual-antenna tightly coupled positioning method and system in complex environments. Based on PPP-RTK, PL and INS multi-source heterogeneous data, a PPP-RTK / PL / INS tightly coupled integrated model is constructed to improve the reliability of navigation parameter estimation in complex scenarios. A multi-source data tightly coupled filtering model is designed based on robust adaptive filtering to improve the model's ability to resist gross errors in complex scenarios, thereby obtaining reliable navigation parameters. An ambiguity fixing scheme is designed based on the characteristics of GNSS satellite and pseudo-satellite constellations, assisted by INS-derived prior information, to improve the convergence speed of navigation parameters in complex scenarios.

Description

Technical Field

[0001] The present invention belongs to the field of joint navigation technology, and in particular relates to a vehicle-mounted dual-antenna tightly coupled positioning method and system in a complex environment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of the Global Navigation Satellite System (GNSS), PPP-RTK (Precise Point Positioning - Carrier Phase Differential Real-Time Kinematic Positioning) technology has become a hot topic in academia and industry. It overcomes the slow convergence speed of PPP, expands RTK's service range, and effectively addresses the problem of large numbers of concurrent users. This technology plays a vital role in next-generation information technologies such as mobile mapping, intelligent driving, and drones, and is a hot topic of research in academia and industry. While PPP-RTK has been extensively studied and achieved promising results, it suffers from GNSS signal-denying environments such as tall buildings, overpasses, roundabouts, tree-lined roads, and tunnels in urban areas, where satellite signals attenuate or even become completely invisible. This prevents PPP-RTK from obtaining continuous and reliable position information. Inertial Navigation Systems (INS) are fully autonomous navigation systems that are immune to external interference and offer high short-term accuracy. However, they suffer from the drawback of error accumulation over time.

[0004] The integration of PPP-RTK and INS complements each other, leveraging the strengths of each system to provide continuous and reliable navigation parameters in complex urban environments. The inclusion of INS information accelerates the convergence of PPP-RTK ambiguity parameters and enables reliable parameter acquisition even when the number of GNSS satellites is low or satellite signal reception is completely unavailable. The combined PPP-RTK / INS model effectively estimates inertial sensor biases and suppresses INS divergence. However, when satellite signals are lost for extended periods, relying solely on INS cannot provide long-term navigation parameters.

[0005] Positioning technology based on pseudolites (PLs) has been widely used in indoor and outdoor scenarios where satellite signals are attenuated due to its flexible base station installation, low external interference, and high positioning accuracy. However, if pseudolites are used alone for positioning, pseudolites are susceptible to interference from errors such as multipath, which can affect positioning performance. Furthermore, since pseudolites are close to users, the observation model is affected by nonlinear errors.

[0006] Therefore, the current observation model is established using a single sensor or two sensor fusions, which cannot provide users with reliable, continuous, high-precision navigation parameter information in complex environments. At the same time, the observation model has weak ability to resist gross errors. Summary of the Invention

[0007] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides a vehicle-mounted dual-antenna PPP-RTK / PL / INS tightly coupled positioning method and system in a complex environment. It uses the IMU (inertial measurement unit) as a bridge to establish a PPP-RTK / PL / INS tightly coupled overall model centered on the IMU. The three heterogeneous data share a filter model, which enhances the strength of the observation model and overcomes the shortcomings of each single sensor and the fusion of two sensors.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A first aspect of the present invention provides a vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment, comprising the following steps:

[0010] Based on PPP-RTK, PL and INS multi-source heterogeneous data, a PPP-RTK / PL / INS tightly coupled integrated observation model is constructed;

[0011] Ambiguity resolution is performed on the integrated observation equation of PPP-RTK / PL / INS integrated navigation to obtain PL ambiguity parameters and GNSS satellite ambiguity parameters. Fixed solution navigation parameter information is obtained based on the fixed PL ambiguity parameters and GNSS ambiguity parameters.

[0012] A double-difference observation model is constructed using the observation values ​​of the GNSS primary antenna and the GNSS auxiliary antenna. A dual-antenna tight coupling constraint model is established based on the double-difference observation model and INS prediction information.

[0013] Identify the carrier's motion state and build a carrier kinematic constraint model based on the carrier's motion state;

[0014] Based on the fixed solution navigation parameter information, the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model and the kinematic constraint model are fused using federal filtering to obtain the final navigation parameter information.

[0015] A second aspect of the present invention provides a vehicle-mounted dual-antenna tightly coupled positioning system in a complex environment, comprising:

[0016] The tightly coupled integrated observation model construction module is configured to: construct a PPP-RTK / PL / INS tightly coupled integrated observation model based on PPP-RTK, PL and INS multi-source heterogeneous data;

[0017] The ambiguity resolution module is configured to: perform ambiguity resolution on the integrated observation equation of the PPP-RTK / PL / INS integrated navigation to obtain PL ambiguity parameters and GNSS satellite ambiguity parameters, and obtain fixed solution navigation parameter information based on the fixed PL ambiguity parameters and GNSS ambiguity parameters;

[0018] The dual-antenna tight coupling constraint model construction module is configured to: construct a double-difference observation model using observations from the GNSS primary antenna and the GNSS auxiliary antenna, and establish a dual-antenna tight coupling constraint model based on the double-difference observation model and INS prediction information;

[0019] The carrier kinematic constraint model construction module is configured to: identify the carrier motion state and construct the carrier kinematic constraint model based on the carrier motion state;

[0020] The federated filtering fusion module is configured to: based on the fixed solution navigation parameter information, use federated filtering to fuse the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model, and the kinematic constraint model to obtain the final navigation parameter information.

[0021] A third aspect of the present invention provides a computer-readable storage medium.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment.

[0023] A fourth aspect of the present invention provides a computer device.

[0024] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment as described above are implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention utilizes PPP-RTK, PL, and INS multi-sensor fusion to construct an integrated model, effectively overcoming the shortcomings of GNSS satellites being blocked, resulting in unreliable positioning or the inability to obtain positioning results, as well as the shortcomings of PL being susceptible to near-far effects and multipath effects, and INS integral operations leading to error accumulation. At the same time, the constraints of GNSS dual-antenna attitude information effectively improve the shortcomings of low observability of heading angle information and enhance the accuracy of heading angle estimation in complex environments. Based on multi-kinematic model constraints, the performance of integrated navigation in obscured scenarios is improved, and the accuracy of parameter estimation is increased. In order to give full play to the performance of the multi-constraint model and improve the efficiency of the filtering algorithm, a federated filtering model is constructed using multi-state constraints. The fusion of GNSS, PL, and INS sensors expands the application field of integrated navigation systems and promotes the development of new generation information technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is a diagram of equipment installation in accordance with the first embodiment of the present invention;

[0029] Figure 2 is a flow chart of the overall method of embodiment 1 of the present invention;

[0030] Figure 3 is a flow chart of the GNSS / PL ambiguity resolution strategy according to the first embodiment of the present invention;

[0031] Figure 4 This is a flow chart of dual-antenna tightly coupled filtering according to the first embodiment of the present invention;

[0032] Figure 5 This is a flow chart of the kinematic constraint model of the first embodiment of the present invention;

[0033] Figure 6 This is the multi-state constrained federated filtering process of the first embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0037] Explanation of terms

[0038] PPP: (precise point positioning) refers to the use of precise satellite orbits and satellite clock errors calculated using GPS observation data from several ground tracking stations around the world to determine the positioning of phase and pseudorange observations collected by a single GPS receiver.

[0039] RTK (Real-time kinematic) is a technology that uses GNSS carrier phase observations for real-time dynamic relative positioning. A GNSS receiver at a base station transmits carrier phase observations and known station coordinates to a rover GNSS receiver via a data communication link. The rover GNSS receiver processes the received signals and collected data in real time to determine the rover's coordinates.

[0040] A PL (Pseudo-Satellite) is a ground-based transmitter that transmits a positioning signal, typically similar to GPS. For this reason, pseudolites are often designed to mimic GPS. Of course, a small number of pseudolites mimic the Galileo or GLONASS systems, and some specialized pseudolites even use custom positioning signal formats.

[0041] An INS (Inertial Navigation System) is an autonomous navigation system that does not rely on external information or radiate energy. It operates in environments including air and ground, as well as underwater. The basic operating principle of INS is based on Newtonian mechanics. By measuring the acceleration of a vehicle in an inertial reference frame, integrating it with time, and transforming it into a navigation coordinate system, information such as velocity, yaw angle, and position in that navigation coordinate system can be obtained.

[0042] Example 1

[0043] Positioning based on pseudolites can be a valuable complement to the PPP-RTK / INS combination. Due to their flexible deployment and strong signal strength, pseudolites can improve the satellite geometry used in PPP-RTK positioning, partially addressing PPP-RTK availability issues. Furthermore, since pseudolites are close to the user, the geometry from the pseudolites to the user changes rapidly as the user receiver moves, accelerating the convergence of ambiguity and position parameters, thus helping to shorten the convergence time for PPP-RTK initialization parameters.

[0044] PPP-RTK also provides relatively high-precision initial positions for pseudolites, mitigating model nonlinearity errors caused by pseudolites being close to the user. Compared to GNSS satellite signals, pseudolites offer higher Doppler accuracy. Fusion of PL and INS can accelerate gyro bias convergence. Furthermore, in scenarios where satellite signals are lost for extended periods, the combination of PL and INS can effectively suppress INS navigation parameter divergence. The INS, in turn, provides high-precision parameter information to assist in pseudolites' positioning. Existing multi-sensor fusion methods are mostly based on pairwise sensor fusion. While there has been research on GNSS / PL / INS multi-sensor fusion algorithms, specific combination models are rarely presented.

[0045] The present invention combines the latest PPP-RTK research hotspots, faces the complex scenes of the city, and carries out the research of the PPP-RTK / PL / INS tightly coupled model. The multi-system fusion of this study uses IMU as a bridge to establish a PPP-RTK / PL / INS tightly coupled overall model centered on IMU. Three heterogeneous data share a filter model, which enhances the strength of the observation model, overcomes the shortcomings of each single sensor and two-by-two sensor fusion, and provides users with more reliable and continuous high-precision navigation parameter information in complex urban environments. At the same time, in order to improve the estimation accuracy of the attitude parameters of low-precision sensors, a dual-antenna tightly coupled constraint model is constructed to increase the external attitude information constraint and improve the navigation parameter estimation accuracy. At the same time, based on the anti-error estimation algorithm, the ability of the observation model to resist gross errors is improved.

[0046] The premise for the implementation of the present invention is to install the GNSS dual antennas, the PL antenna and the inertial sensor on a carrier.

[0047] like Figure 1 As shown, the equipment is installed as Figure 1As shown, the vehicle's forward direction is defined as the y-axis, the vehicle's lateral direction is defined as the x-axis, and the vehicle's celestial direction is defined as the z-axis. This coordinate system is the carrier coordinate system. The GNSS dual antenna includes a GNSS main antenna and a GNSS auxiliary antenna. The GNSS main antenna and the GNSS auxiliary antenna are spaced apart, and the baseline arrangement direction is the y-axis in the vehicle's forward direction. The inertial sensor is placed between the GNSS main antenna and the GNSS auxiliary antenna. The pseudolite antenna is placed between the GNSS main antenna and the inertial sensor and is close to the inertial sensor. The inertial sensor and the arm between the dual GNSS antenna and the pseudolite antenna are calibrated in advance.

[0048] like Figure 2 As shown, this embodiment provides a dual-antenna fusion positioning method for vehicles with multiple kinematic models constraints in complex environments, including the following steps:

[0049] S101: Based on PPP-RTK, PL and INS multi-source heterogeneous data, a PPP-RTK / PL / INS tightly coupled integrated model is constructed;

[0050] S102: Resolving the ambiguity of the integrated observation equation of the PPP-RTK / PL / INS integrated navigation; using the INS predicted position information as a constraint, searching for fixed PL ambiguity parameters, using the fixed PL ambiguity as a constraint to assist in resolving the GNSS ambiguity, resolving the GNSS satellite ambiguity, and obtaining fixed solution navigation parameter information of the PPP-RTK / PL / INS integrated system based on the fixed PL ambiguity and the GNSS satellite ambiguity;

[0051] S103: Build a double-difference observation model using the observation values ​​of the GNSS primary antenna and the GNSS auxiliary antenna; and establish a dual-antenna tightly coupled constraint model based on the double-difference observation model and INS prediction information.

[0052] S104: Identify the motion state of the carrier and construct a carrier kinematic constraint model based on the motion state of the carrier;

[0053] S105: Based on the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system, a federated filter is used to fuse the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model, and the kinematic constraint model to obtain the final navigation parameter information.

[0054] In S101, based on PPP-RTK, PL, and INS multi-source heterogeneous data, a PPP-RTK / PL / INS tightly coupled integrated model is constructed, including:

[0055] S201: Build an undifferenced, uncombined PPP model using pseudorange, carrier, and Doppler observations collected in real time by the GNSS primary antenna. Modify the undifferenced, uncombined PPP-RTK observation equations based on pseudorange and carrier observations using real-time state space correction information broadcast by the regional reference station network.

[0056] S202: Using the real-time pseudorange observation values ​​and carrier phase observation values ​​of the PL observation, an undifferenced, non-combined PL observation equation is established; after correction for the uncalibrated hardware delay fractional cycle bias (FCB) and antenna phase center, a revised PL observation equation is obtained.

[0057] S203: Perform initial alignment of the INS. After initial alignment, perform strapdown solution using the specific force and angular velocity observation information, and substitute the solved position and velocity information into the PPP-RTK observation equation and the PL observation equation. Linearize the observation equation to obtain the PPP-RTK / PL / INS tightly coupled integrated observation model.

[0058] In S201, a non-differenced non-combined PPP observation model based on the pseudorange, carrier and Doppler observation values ​​received by the GNSS main antenna is established. The expression of the non-differenced non-combined PPP observation model based on the pseudorange, carrier and Doppler observation values ​​is:

[0059]

[0060]

[0061]

[0062] Where, s represents the satellite number, i represents the receiver number, Q represents the satellite system; P, and D represent pseudorange observations, carrier phase observations, and Doppler observations; Indicates the rate of change of satellite-to-ground distance; dt s represents the satellite clock error, and are the rates of change of receiver clock error and satellite clock error respectively; ZWD i is the zenith wet delay and wet delay change rate of the station; is the wet projection function; represents the rate of change of tropospheric delay; and denote the slant ionospheric delay and the rate of change of slant ionospheric delay respectively; γ is the frequency-dependent ionospheric delay amplification factor; and d s are the uncorrected pseudorange hardware delays at the receiver and satellite ends, respectively; and bs are the uncorrected phase hardware delays of the receiver and satellite, respectively; N represents the ambiguity; and denote the pseudorange, carrier phase and Doppler observation noise and other unmodeled errors, respectively.

[0063] The expression of the undifferenced, non-combined PPP observation equation based on pseudorange and carrier observations is modified by the real-time state space correction information broadcast by the regional reference station network:

[0064]

[0065]

[0066] Where, is the pseudo-range observation correction value broadcast by the PPP-RTK center, This is the carrier observation correction value broadcast by the PPP-RTK center.

[0067] Equations (3), (4), and (5) above are the established undifferenced, uncombined PPP-RTK observation equations. Using the undifferenced, uncombined PPP-RTK observation equations, the parameters to be estimated in this model are the three position parameters, the three velocity parameters, the receiver clock error, the tropospheric wet delay, and the ambiguity parameter.

[0068] In S202, the PL observation equation is established using the corrected observation value:

[0069]

[0070]

[0071] Where PL represents pseudolite, and the other symbols have the same meanings as in equations (1) and (2).

[0072] In S203, the PPP-RTK observation equations (3), (4), and (5) of the GNSS satellites and the PL observation equations (6) and (7) are combined to construct a PPP-RTK / PL integrated model, and the joint observation equations are linearized at the center position of the IMU.

[0073] According to equations (6) and (7), the three position parameters, receiver clock error, tropospheric wet delay and ambiguity parameters are obtained.

[0074] At this time, it is necessary to consider the arm l from the center of the GNSS main antenna to the center of the IMU g , and the PL antenna center to the IMU center arm l P Considering the influence of the lever arm l g and l PThe corrected PPP-RTK and PL observation models are:

[0075]

[0076] Where e, n and b represent the Earth-centered Earth-fixed coordinate system, navigation coordinate system and carrier coordinate system respectively; and They represent the position of the IMU center predicted by INS in the e frame, the position of the GNSS satellite in the e frame, and the position of the pseudolite in the e frame respectively; and They represent the speed of the IMU center in the e-frame predicted by INS and the speed of the satellite in the e-frame respectively; l g and l P Respectively represent the arm distance from the GNSS antenna center and the PL antenna center to the IMU center; ΔP1, ΔL1 and denote the residual errors of pseudorange, carrier phase and Doppler observation equations respectively; Represents the direction cosine matrix from n to e; Represents the direction cosine matrix from b to n; Represents the projection of the angular velocity of the navigation coordinate system relative to the inertial coordinate system in the navigation coordinate system.

[0077] After perturbation processing of the PPP-RTK / PL / INS combined observation equation, the coefficient matrix H of the PPP-RTK / PL / INS tightly coupled observation equation can be obtained: TC for:

[0078]

[0079] in, H2=B g C1, H7=B P C1.

[0080] Where, Represents the coefficient matrix of the GNSS satellite observation equation; Represents the coefficient matrix of the pseudo-satellite observation equation; C1 represents the direction cosine matrix that converts the position correction number in the geographic coordinate system to the position correction number in the spatial rectangular coordinate system.

[0081] The GNSS PPP-RTK / PL / INS tightly coupled observation model is constructed by equations (8) and (9), where the unknowns X include navigation parameters and INS parameters:

[0082]

[0083] Where, φn Denotes the misalignment angle, δb g and δb a They represent gyro bias and acceleration bias errors respectively.

[0084] In S102, since the distance between the PL and the user is relatively close, the geometric structure between the PL and the user changes rapidly, which is conducive to the convergence of the ambiguity parameters. However, the GNSS is far away from the user and is more affected by the outside world during signal propagation, so the ambiguity parameters converge more slowly. This embodiment proposes to first fix the ambiguity parameters of the PL, and then use them as constraints to fix the GNSS satellite ambiguity. The implementation process can be as follows: Figure 3 express.

[0085] First, the PPP-RTK / PL / INS tightly coupled observation equation (8) is established using the observation data of the GNSS main antenna and the PL observation data.

[0086] The parameter information predicted by INS has the characteristics of short-term high precision, which is used as a constraint to assist in solving the navigation parameters of the observation equation (8). The constraint equation constructed by INS prediction information is:

[0087]

[0088] The observation equations composed of Equations (8) and (11) are filtered and solved using the Extended Kalman Filter (EKF). Based on the floating-point solution of the PL ambiguity parameters and the corresponding variance-covariance matrix, the LAMBDA algorithm is used to search for the fixed PL ambiguity parameters. Once the PL ambiguity parameters are fixed, they can be substituted into the observation equation as constraints to solve the GNSS satellite ambiguity parameters:

[0089]

[0090] Since the pseudolite ambiguity parameters have been resolved, the unknown parameters of the PPP-RTK / PL / INS tightly coupled model are now:

[0091]

[0092] The coefficient matrix of formula (12) becomes:

[0093]

[0094] Using equations (12) and (13) to perform filtering, the ambiguity parameters of fixed GNSS satellites are searched using the LAMBDA algorithm based on the floating-point solution of the GNSS satellite ambiguity parameters and the corresponding variance-covariance matrix. The fixed PL ambiguity and GNSS ambiguity are used as constraints to solve the navigation parameters of the PPP-RTK / PL / INS observation equations, and the ambiguity fixed solution navigation parameters X are obtained. GPI and the corresponding variance matrix ∑ GPI .

[0095] In S103, in order to improve the accuracy of attitude parameters, especially heading parameter estimation, the present invention uses GNSS dual antennas to establish a tightly coupled constraint model, and its implementation process is as follows: Figure 4 shown.

[0096] The double-difference observation model constructed by using the observation values ​​of the GNSS primary antenna and the GNSS auxiliary antenna includes:

[0097] Assume A1 is the main antenna, A2 is the auxiliary antenna, and the visible satellites are p and q, then the inter-station single-difference observation equation can be expressed as:

[0098]

[0099] Where, represents the carrier phase observation value, Indicates the geometric distance between the visible satellite p and the auxiliary antenna - the satellite-to-earth distance, It represents the geometric distance between the visible satellite p and the main antenna - the satellite-to-earth distance, represents the fuzziness parameter, represents the receiver clock error, I represents the ionospheric delay parameter, and T represents the tropospheric delay parameter; represents the single-difference carrier phase observation noise between stations, and Δ represents the single-difference operator; represents the carrier phase observation noise.

[0100] According to formula (14), the double difference observation equation can be obtained, and the baseline initial value After expansion and sorting:

[0101]

[0102] After solving equation (15) and searching for fixed ambiguity, the baseline equation can be obtained as:

[0103]

[0104] In order to use the GNSS dual-antenna baseline equation as a constraint to assist in the calculation of PPP-RTK / PL / INS tightly coupled navigation parameters, the inconsistency between the GNSS antenna center and the IMU center must be considered. Based on the sensor installation method, the arm vector from the GNSS antenna center to the IMU center in the carrier coordinate system is l g =[0 b y 0] T .

[0105] Assume that the arm from the inertial sensor to the GNSS main antenna A1 is The arm from the inertial sensor to the GNSS auxiliary antenna A2 is Taking into account the influence of the pole arm, the coordinates of antennas A1 and A2 in the Earth-centered Earth-fixed coordinate system can be expressed as:

[0106]

[0107] Where, and They represent the real coordinates of antennas A1 and A2 in the Earth-fixed system, Represents the true coordinates of the inertial sensor in the Earth-centered Earth-fixed coordinate system, Represents the direction cosine matrix of the carrier system to the Earth-centered Earth-fixed coordinate system.

[0108] The position measurements of antennas A1 and A2 can be expressed as:

[0109]

[0110] Where, and represents the measured values ​​of the positions of GNSS antennas A1 and A2, ε r1 and ε r2 represents the corresponding measurement error.

[0111] The difference between equation (17) and equation (18) can be expressed as:

[0112]

[0113] The direction cosine matrix will be calculated With the real direction cosine matrix Substituting the relationship between into formula (19) we can get:

[0114]

[0115]

[0116] Subtracting equation (20) from equation (21) yields the following baseline error equation:

[0117]

[0118] Where, Calculated by INS, Calculated by GNSS dual antennas.

[0119] Since antennas A1 and A2 are close to each other, the residual atmospheric error after double difference can be ignored. Combining equations (16) and (22), the dual antenna constraint equation can be obtained as follows:

[0120]

[0121] Formula (23) is the constructed dual-antenna GNSS baseline constraint tight coupling model. By performing tight coupling filter solution on Formula (23), the dual-antenna GNSS / INS tight coupling navigation parameter X can be obtained. GGI and the corresponding variance matrix ∑ GGI .

[0122] In S104, the motion state of the carrier is identified, and a kinematic constraint model is constructed based on the motion state of the carrier, including:

[0123] The model constructed based on the vehicle motion state can well assist in estimating navigation parameters. Its implementation process is as follows: Figure 5 shown.

[0124] The motion state of the carrier is identified using the three-axis acceleration information, three-axis angular velocity information and carrier velocity information of the inertial sensor; when the carrier is identified as stationary motion, the zero-speed update and zero-angular velocity update models are used as constraint models; when the carrier is judged to be stationary, the non-holonomic constraint algorithm is used as the constraint model.

[0125] Based on the acceleration and angular velocity information output by the inertial sensor and the speed information calculated by PPP-RTK, it is possible to identify whether the vehicle is stationary or moving in a straight line.

[0126] The three-axis gyroscope and accelerometer information in the sliding window is used to determine whether the carrier is in a stationary state. The judgment formula is:

[0127]

[0128] Where ω and f represent angular velocity and acceleration; and Represents the average value of angular velocity and acceleration in the corresponding window.

[0129] When the carrier is judged to be stationary, the zero speed update and zero angular rate update algorithms are used:

[0130]

[0131]

[0132] By filtering and updating equations (25) and (26) respectively, we can obtain the navigation parameter X ZUPT and X ZARU and the corresponding variance matrix ∑ZUPT and ∑ ZARU .

[0133] When the carrier is judged to be moving in a straight line, the nonholonomic constraint algorithm based on the assumption that the lateral and celestial velocities are zero can work and improve the accuracy of the navigation parameters. However, when the carrier is in a turning motion, the constraint performance of the nonholonomic constraint algorithm is reduced.

[0134] First, determine whether the carrier is in linear motion based on the angular velocity information and vehicle speed information:

[0135]

[0136]

[0137] Where, Indicates the change of heading angle; t is the sampling interval, T is the window length; δ is the threshold.

[0138] When the vehicle motion is judged to be moving along a straight line, an auxiliary equation of nonholonomic constraints is constructed to improve the accuracy of navigation parameter estimation:

[0139]

[0140] Perform nonholonomic constraint filtering and updating on Equation (29) to obtain the navigation parameter X NHC and the corresponding variance matrix ∑ NHC .

[0141] In S105, the present invention employs multiple constraint models, and the quality of each constraint varies depending on the scenario. Dual-antenna attitude constraints perform well in scenarios with good observation environments, but their performance degrades in scenarios with severe occlusion. Nonholonomic constraints perform well when the vehicle is traveling in a straight line, and zero-speed correction and zero-angular-rate updates perform well when the vehicle is stationary.

[0142] In order to make full use of multiple constraint information and improve computational efficiency, the present invention uses federated filtering to fuse multiple constraint information. The implementation process is as follows: Figure 6 shown.

[0143] The PPP-RTK / PL integrated model, baseline constraint model, and carrier kinematic constraint model are constructed as sub-filtering systems, and the filtering and updating solutions are performed on each sub-system to obtain the navigation parameters X of each sub-system. GPI 、X GGI 、X ZUPT 、X ZARU and X NHC And the corresponding variance matrix ∑ GPI ,∑ GGI ,∑ZUPT ,∑ ZARU and ∑ NHC , and then fuse the navigation parameters and variance information of each sub-filter system to obtain:

[0144]

[0145] Where ∑ represents the filter variance; i and k represent the sub-filter and epoch, respectively.

[0146] When performing information fusion on each sub-filter system, each sub-filter is adjusted by the information allocation coefficient:

[0147]

[0148] Where Q represents the process noise variance; β i represents the distribution coefficient, ∑β i =1.

[0149] The distribution coefficient is obtained by New information Sure:

[0150]

[0151] Where, Indicates new information; W=HΣ k,k-1 H T +R, R represents the observation noise matrix.

[0152] In order to better resist the influence of the subsystem with large gross errors on parameter estimation, the distribution coefficient is adjusted using the robustness weight function.

[0153]

[0154] Where c1 and c2 are constants, c1 = 0.85 ~ 1.0, c2 = 8 ~ 10; ΔV k for:

[0155]

[0156] In order to ensure the conservation of the distribution factor, the adjusted distribution factor is normalized.

[0157]

[0158] Example 2

[0159] This embodiment discloses a vehicle-mounted dual-antenna tightly coupled positioning system in a complex environment, including:

[0160] The tightly coupled integrated observation model construction module is configured to: construct a PPP-RTK / PL / INS tightly coupled integrated observation model based on PPP-RTK, PL and INS multi-source heterogeneous data;

[0161] The ambiguity resolution module is configured to: perform ambiguity resolution on the integrated observation equation of the PPP-RTK / PL / INS integrated navigation to obtain PL ambiguity parameters and GNSS satellite ambiguity parameters, and obtain fixed solution navigation parameter information based on the fixed PL ambiguity and GNSS satellite ambiguity parameters;

[0162] The dual-antenna tight coupling constraint model construction module is configured to: construct a double-difference observation model using observations from the GNSS primary antenna and the GNSS auxiliary antenna, and establish a dual-antenna tight coupling constraint model based on the double-difference observation model and INS prediction information;

[0163] The carrier kinematic constraint model construction module is configured to: identify the carrier motion state and construct the carrier kinematic constraint model based on the carrier motion state;

[0164] The federated filtering fusion module is configured to: based on the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system, use federated filtering to fuse the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model, and the kinematic constraint model to obtain the final navigation parameter information.

[0165] Example 3

[0166] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment are implemented as described above.

[0167] Example 4

[0168] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment as described above are implemented.

[0169] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0174] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment, characterized by: The steps include: Based on PPP-RTK, PL and INS multi-source heterogeneous data, a PPP-RTK / PL / INS tightly coupled integrated observation model is constructed; Ambiguity resolution is performed on the integrated observation equations of the PPP-RTK / PL / INS integrated navigation to obtain fixed PL ambiguity parameters and GNSS satellite ambiguity parameters. Based on the fixed PL ambiguity parameters and GNSS satellite ambiguity parameters, the fixed solution navigation parameter information of the PPP-RTK / PL / INS integrated system is obtained. The ambiguity resolution of the PPP-RTK / PL / INS integrated navigation observation equation includes: Using the INS-predicted position information as a constraint, the fixed PL ambiguity parameters are searched, and the fixed PL ambiguity is used as a constraint to assist in solving the GNSS satellite ambiguity. Based on the fixed PL ambiguity and GNSS satellite ambiguity, the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system is obtained. A double-difference observation model is constructed using the observation values ​​of the GNSS primary antenna and the GNSS secondary antenna. A dual-antenna tight coupling constraint model is established based on the double-difference observation model, INS prediction information, and the arm information from the inertial sensor to the primary antenna and the secondary antenna. Identify the carrier's motion state and build a carrier kinematic constraint model based on the carrier's motion state; Based on the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system, the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model, and the kinematic constraint model are federated and fused to obtain the final navigation parameter information. The process of federated filtering fusion is as follows: The PPP-RTK / PL / INS integrated model, the dual-antenna baseline constraint tightly coupled model, and the carrier kinematic constraint model are constructed as sub-filtering systems. Each subsystem is filtered and updated to obtain the navigation parameters and corresponding variance matrix of each subsystem. The navigation parameters and variance information matrices of each sub-filtering system are fused, and each sub-filter is adjusted by the information distribution coefficient during the fusion.

2. The vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment as claimed in claim 1, characterized in that: The construction of a PPP-RTK / PL / INS tightly coupled integrated observation model based on PPP-RTK, PL and INS multi-source heterogeneous data includes: Obtain GNSS observation data to construct PPP-RTK observation equations; Obtain pseudorange observation values ​​and carrier phase observation values ​​of PL observation and establish PL observation equation; The INS is initially aligned. After the initial alignment, the strapdown solution is performed using the specific force and angular velocity observation information. The solved position and velocity information are substituted into the PPP-RTK observation equation and the PL observation equation. The observation equation is linearized to obtain the PPP-RTK / PL / INS tightly coupled integrated observation model.

3. The vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment as claimed in claim 1, characterized in that: The GNSS dual antenna includes a GNSS primary antenna and a GNSS secondary antenna. The process of constructing the baseline equation of the GNSS dual antenna is as follows: The double-difference observation equation is obtained based on the expression of the single-difference observation equation between stations, and is expanded at the initial value of the baseline. After solving and searching for fixed ambiguities, the baseline equation of the GNSS dual antenna is obtained.

4. The vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment as claimed in claim 1, characterized in that: Identify the carrier's motion state and build a kinematic constraint model based on the carrier's motion state, including: The motion state of the carrier is identified using the three-axis acceleration information, three-axis angular velocity information and carrier velocity information of the inertial sensor; when the carrier is identified as stationary motion, the zero-speed update and zero-angular velocity update models are used as constraint models; when the carrier is judged to be stationary, the non-holonomic constraint algorithm is used as the constraint model.

5. A vehicle-mounted dual-antenna tightly coupled positioning system in complex environments, characterized by: include: The tightly coupled integrated observation model construction module is configured to: construct a PPP-RTK / PL / INS tightly coupled integrated observation model based on PPP-RTK, PL and INS multi-source heterogeneous data; The ambiguity resolution module is configured to: perform ambiguity resolution on the integrated observation equation of the PPP-RTK / PL / INS integrated navigation to obtain fixed PL ambiguity parameters and GNSS satellite ambiguity parameters, and obtain fixed solution navigation parameter information of the PPP-RTK / PL / INS integrated system based on the fixed PL ambiguity parameters and GNSS satellite ambiguity parameters; The ambiguity resolution of the PPP-RTK / PL / INS integrated navigation observation equation includes: Using the INS-predicted position information as a constraint, the fixed PL ambiguity parameters are searched, and the fixed PL ambiguity is used as a constraint to assist in solving the GNSS satellite ambiguity. Based on the fixed PL ambiguity and GNSS satellite ambiguity, the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system is obtained. The dual-antenna tight coupling constraint model construction module is configured to: construct a double-difference observation model using observations from the GNSS primary antenna and the GNSS secondary antenna, and establish a dual-antenna tight coupling constraint model based on the double-difference observation model, INS prediction information, and arm information from the inertial sensor to the primary antenna and the secondary antenna; The carrier kinematic constraint model construction module is configured to: identify the carrier motion state and construct the carrier kinematic constraint model based on the carrier motion state; The federated filtering fusion module is configured to: Based on the fixed solution navigation parameter information of the PPP-RTK / PL / INS combined system, use federated filtering to fuse the PPP-RTK / PL / INS tightly coupled model, the dual-antenna baseline constraint tightly coupled model, and the kinematic constraint model to obtain the final navigation parameter information; The process of federated filtering fusion is as follows: The PPP-RTK / PL / INS integrated model, the dual-antenna baseline constraint tightly coupled model, and the carrier kinematic constraint model are constructed as sub-filtering systems. Each subsystem is filtered and updated to obtain the navigation parameters and corresponding variance matrix of each subsystem. The navigation parameters and variance information matrices of each sub-filtering system are fused, and each sub-filter is adjusted by the information distribution coefficient during the fusion.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment are implemented as described in any one of claims 1 to 4.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the vehicle-mounted dual-antenna tightly coupled positioning method in a complex environment are implemented as described in any one of claims 1-4.

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