A combined navigation system of GNSS / SINS combined with vehicle CAN bus speed information

Abstract

The application relates to the technical field of navigation, in particular to a combined positioning system for realizing GNSS / SINS combined vehicle CAN bus speed information, which is based on a MEMS-IMU and a GNSS receiving module, can obtain required inertial navigation calculation information and GNSS positioning information in real time, can obtain vehicle speed information through an OBD port of the vehicle, and finally can obtain a fusion positioning result by PC processing of the parameters. The combined navigation algorithm principle is based on a fusion positioning algorithm of an extended Kalman filter, and a non-integrity constraint algorithm and a zero-speed correction technology are introduced. The application enhances the robustness of the combined navigation system, and can better meet the demand for high precision and high stability of vehicle positioning.

Description

Technical Field

[0001] This invention relates to the field of navigation technology, and more specifically to a combined positioning system that integrates GNSS / SINS with vehicle CAN bus speed information. Background Technology

[0002] Integrated navigation and positioning systems are widely used in various fields and form the foundation for the development of vehicle assistance industries such as autonomous driving. Global Navigation Satellite Systems (GNSS) can acquire real-time and all-weather positioning and orientation information through communication between satellites and signal receivers; however, their positioning accuracy is constrained by a range of environmental conditions. Strapdown Inertial Navigation Systems (SINS) are devices that can achieve vehicle positioning and orientation through their own calculations, independently acquiring the necessary information without external interaction. However, they lack external information for correction, and low-precision hardware, in particular, suffers from significant cumulative errors. These two systems each have their own advantages and disadvantages, and their characteristics complement each other, thus allowing them to be designed for combined operation.

[0003] In urban driving environments, GNSS signals are highly susceptible to interference or obstruction, leading to significant errors in the integrated navigation results and preventing it from meeting positioning requirements. Therefore, additional components are typically added to integrated navigation systems to enhance system stability. The most common addition is an odometer, which uses odometer observations to constrain the inertial navigation unit's calculations when GNSS signals are unavailable.

[0004] Currently, speed information obtained by commonly used external odometers is calculated by the difference in pulse counts between two sampling intervals, along with the odometer encoder's resolution and wheel diameter. This method results in significant errors in the odometer speed, typically requiring the accumulation of data over a period before averaging. This leads to delays in the calculated speed and a low output frequency, reducing the effectiveness of odometer information in constraining real-time integrated positioning and navigation systems.

[0005] Conventional Kalman filtering schemes in information fusion cannot match nonlinear environments. In urban vehicle driving environments, the system's measurement noise is not always approximately Gaussian white noise. In some noisy areas, the nonlinearity of the noise is strong and varies, which can lead to significant errors in the filtering results. Summary of the Invention

[0006] Purpose of the invention

[0007] To address the issues of low accuracy and susceptibility to environmental obstruction and interference in current consumer-grade GNSS / SINS integrated navigation systems, this invention aims to provide a combined positioning system that integrates GNSS / SINS with vehicle CAN bus speed information, effectively ensuring performance in various environments.

[0008] Technical solution

[0009] A combined positioning system integrating GNSS / SINS and vehicle CAN bus speed information is based on MEMS-IMU and GNSS receiving module. It acquires the required inertial navigation solution information and GNSS positioning information in real time, obtains the vehicle speed information through the vehicle's OBD port, and finally processes these parameters by PC to obtain the fused positioning result.

[0010] The integrated positioning system includes: a SINS module, a GPS module, a CAN data transceiver module, and an integrated navigation computing unit;

[0011] The SINS module includes an inertial measurement unit and a pose calculation module;

[0012] The inertial measurement unit includes a gyroscope and an accelerometer, wherein

[0013] The gyroscope measures and acquires the angular velocity information of the carrier. By determining the initial angle through initial alignment, the coordinate transformation matrix from the carrier coordinate system to the navigation coordinate system at subsequent moments can be calculated.

[0014] The accelerometer measures and acquires acceleration information, and the acceleration components mapped onto the three axes of the navigation coordinate system are obtained through a coordinate transformation matrix.

[0015] The pose calculation module includes a strapdown inertial navigation algorithm module, which calculates the carrier's attitude angles, velocity, and position information using the angular velocity and acceleration information obtained from the inertial measurement unit. The SINS module can only measure the changes in the carrier's attitude angles, velocity, and position relative to its initial position. Through the initial alignment of the strapdown inertial navigation system, the initial latitude, longitude, and elevation information of the carrier, as well as the angles between the three axes of the carrier's coordinate system and the navigation coordinate system, are known. This allows for real-time acquisition of the carrier's navigation and positioning information during subsequent motion.

[0016] The GPS module includes a GNSS receiver, which integrates an RF chip, a baseband chip, and a processing chip, and is connected to an external antenna to receive and decode signals emitted by satellites in order to calculate the three-dimensional coordinates of the observation point; for a moving vehicle, its speed information can be deduced by the changes in three-dimensional coordinate information at some continuous moments; this module is existing technology.

[0017] The CAN data transceiver module includes a CAN transceiver chip. The CAN data transceiver module connects the integrated navigation computing unit and the vehicle OBD port (an external module, not part of this invention), and connects to the vehicle CAN bus (an external module, not part of this invention) through the vehicle OBD port. The CAN transceiver chip enables communication between the integrated navigation computing unit and the vehicle CAN bus. The integrated navigation computing unit sends instructions to the CAN bus and receives data packets containing vehicle speed information via the CAN transceiver chip.

[0018] The integrated navigation calculation unit includes an MCU microcontroller that runs the integrated navigation algorithm and completes the calculations for integrated navigation.

[0019] The integrated navigation algorithm is based on the fusion positioning algorithm of extended Kalman filtering:

[0020] When the number of satellites received by the satellite signal receiver and the GNSS accuracy coefficient are within the set range, the satellite signal is considered valid. When the satellite signal is valid, the difference between the position and velocity output by the receiver and the position and velocity output by the INS forms the observed measurement of the carrier's position and velocity in the fusion filtering algorithm. When the satellite signal is invalid, the difference between the velocity within the CAN and the output velocity of the INS forms the observed measurement of the carrier's velocity, and the filtering result is calculated. An adaptive extended Kalman filter algorithm is used to adaptively update the environmental noise covariance matrix during state updates. and the environmental noise covariance matrix during measurement updates This allows the two key parameters for updating the extended Kalman filter to adjust autonomously according to different environmental conditions, thereby reducing errors.

[0021] Furthermore, when a vehicle is in motion, it will not jump off the ground or slip on the ground. The velocity components in the plane perpendicular to the vehicle's direction of travel are all equal to zero. A non-integrity constraint algorithm is designed to limit the velocity update of the inertial navigation system. In actual driving, there are relatively frequent stopping situations. When the detected GNSS velocity or the inertial navigation solution velocity is less than this threshold, it can be considered that the vehicle has not moved.

[0022] Furthermore, a zero-velocity detection algorithm is designed, which uses only the difference between the velocity information calculated by inertial navigation and the velocity information obtained by GNSS as the observation, and takes the average value of the position and attitude angle information over the previous time period to reduce the accumulation of errors in inertial navigation.

[0023] The system workflow includes the following steps:

[0024] Step 1: Based on the development board with integrated MEMS-IMU and GNSS receiver modules, acquire the required inertial navigation calculation information and GNSS positioning information in real time;

[0025] Step 2: The development board communicates with the CAN bus via the vehicle's OBD port, sending commands and receiving returned data frames. The development board can also connect to an external PC wirelessly or via wired connection to transmit and display the positioning results in real time.

[0026] Step 3: The development board's integrated navigation calculation unit evaluates the validity of the acquired satellite signals based on the GNSS signal status information obtained by the GNSS signal receiving module, such as the number of received satellites and the accuracy factor. When the satellite signals are valid, an adaptive extended Kalman filter fusion algorithm is used to combine the GNSS positioning results with the inertial navigation solution results; when the satellite signals are determined to be invalid, a dead reckoning algorithm is used to fuse the inertial navigation solution results and OBD velocity information to obtain the positioning result.

[0027] Step 4: Based on the actual movement of the vehicle, non-integrity constraints and vehicle kinematic constraints are used to constrain the parameters in the fusion filtering algorithm to improve the positioning accuracy.

[0028] Technical effect

[0029] The advantages of this invention are:

[0030] (1) This invention proposes a fusion positioning algorithm based on extended Kalman filtering. When the satellite signal reception is good, the positioning system is composed of a satellite signal receiver and an inertial navigation element. When the satellite signal is distorted or cannot be received, the vehicle speed and inertial navigation obtained by other sensors are combined. The two systems are independent of each other and do not interfere with each other. They can be applied to different scenarios, and the robustness of the integrated navigation system is enhanced while maintaining the loose coupling flexibility of GNSS / SINS.

[0031] (2) The vehicle speed information in the vehicle CAN bus is obtained through the vehicle OBD port and used as auxiliary information when the GNSS signal is missing. Compared with the conventional external odometer, the output delay is reduced and the output frequency is increased, realizing real-time communication and online filtering, and meeting the requirements of high precision and high stability of vehicle positioning.

[0032] (3) In the fusion positioning algorithm, a simplified adaptive filtering algorithm is proposed to meet the situation where the environmental noise changes; in addition, vehicle kinematic constraints are added to the algorithm, and a non-holonomic constraint algorithm and a zero-speed detection algorithm are proposed to correct the vehicle's pose information, making it more consistent with the actual vehicle's motion model and improving the overall positioning accuracy. Attached Figure Description

[0033] Figure 1 This is a functional block diagram of a combined positioning system for GNSS / SINS / vehicle CAN bus speeds according to the present invention;

[0034] Figure 2This is a flowchart illustrating the operation of a combined positioning system for GNSS / SINS / vehicle CAN bus speeds according to the present invention.

[0035] Figure 3 The images show the positioning trajectory (solid line) and reference trajectory (dashed line) of a common GPS / SINS navigation system measured in an embodiment of the present invention.

[0036] Figure 4 The images show the positioning trajectory (solid line) and reference trajectory (dashed line) of a normal GNSS / SINS navigation system when GPS signals are lost on certain road sections during actual measurements in this embodiment of the invention.

[0037] Figure 5 The images show the system positioning trajectory (solid line) and reference trajectory (dashed line) of this invention when GPS signals are lost on certain road sections during actual testing in this embodiment of the invention. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings.

[0039] This invention discloses a combined positioning system that integrates GNSS / SINS with vehicle CAN bus speed information, such as... Figure 1 As shown, it includes SINS module 1, GPS module 2, CAN data transceiver module 3, and integrated navigation computing unit 4;

[0040] In SINS module 1, the inertial measurement elements, gyroscope and accelerometer, measure the angular rate and specific force of the carrier, respectively. Using common inertial navigation quaternion calculation methods, the real-time updated Euler angles and coordinate transformation matrix can be obtained. The specific force obtained from the accelerometer is integrated to obtain the velocity update, and a second integration yields the position update. The calculation formulas are as follows:

[0041]

[0042] in, , , These are the three-axis accelerations of the carrier coordinate system. The acceleration calculated by the accelerometer. It is the transformation matrix from the navigation coordinate system to the vehicle coordinate system;

[0043]

[0044] in, , , These are the latitude, longitude, and elevation of the carrier, respectively. , , Download the three-axis velocities of the volume in the navigation coordinate system. It is the radius of curvature of the zonal circle. It is the radius of curvature of the meridian;

[0045] In GPS module 2, the GNSS antenna transmits the received satellite signals to the radio frequency front end through the data channel. After processing by the radio frequency front end, interference suppression module, baseband processing unit, etc., the decoded GNSS positioning information is transmitted outward.

[0046] CAN data transceiver module 3 can automatically encode and decode CAN data. It only needs to query parameters such as vehicle speed according to the CAN protocol to receive feedback.

[0047] The integrated navigation calculation unit 4 includes an MCU microcontroller that runs integrated navigation algorithms; the integrated navigation algorithms include: an adaptive extended Kalman filter algorithm when GNSS signals are valid and a dead reckoning algorithm when GNSS signals are invalid;

[0048] Adaptive Extended Kalman Filter Algorithm:

[0049] The adaptive extended Kalman filter algorithm consists of two parts: state update and measurement update. Both GNSS and inertial navigation systems can obtain the vehicle's velocity and position information. The inertial navigation system has a high calculation frequency, while the GNSS signal output frequency is low. The inertial navigation calculation part is used as the basic system, and the satellite signal acquired by the satellite receiver is used as a reference to correct the inertial navigation parameters, resulting in a fusion result. The system state equation and measurement equation are expressed as follows:

[0050]

[0051]

[0052] in, Is The system state variables at time t, and the system observation vector are: , and These are the system noise figure and the system noise matrix, respectively. It is the system measurement matrix. It is a measurement noise matrix. and These are the system state update and the environmental noise during the observation period, respectively. The system state transition matrix is ​​determined by this algorithm. , , , These are the three-axis velocity vectors and three-axis position vectors obtained from INS and GNSS calculations in the navigation coordinate system, respectively. The system state transition matrix is ​​also shown. Represented as:

[0053]

[0054] in, , , It is the coefficient matrix of the angle, velocity, and position terms in the attitude error equation during inertial navigation system (INS) calculation. , , It is the coefficient matrix of the angle, velocity, and position terms in the velocity error equation. , This is the coefficient matrix of the velocity and position terms in the position error equation. System measurement matrix. System noise figure System noise matrix With measurement noise matrix They can be represented as:

[0055]

[0056]

[0057]

[0058]

[0059] in , These are the random walk error vectors for the gyroscope and accelerometer, respectively. , These are the noise vectors for velocity measurement and position measurement at the GNSS receiver, respectively.

[0060] The Extended Kalman Filter (EKF) model is used to transform some problems where nonlinearity is not particularly pronounced into linear problems that are easier to handle. The EKF is expanded using the Taylor series, and by taking the first-order term, the filtering formula for the nonlinear system state can be expressed as:

[0061]

[0062] in, Let covariance be the state variable. and These are the system state update and environmental noise covariance during the observation period, respectively. This represents the Kalman filter gain; other variables are the same as above. respectively dimensional state variables Subsequent Taylor expansion. The Taylor formula can be expanded as follows:

[0063]

[0064] in express of First derivative, It is the remainder term of Taylor's formula.

[0065] The system's state variables are 15-dimensional, representing the errors in the carrier's velocity, position, and attitude, as well as the deviations of the accelerometer and gyroscope axes:

[0066]

[0067] in , , These are the attitude angle errors calculated by the inertial measurement unit and mapped onto the three coordinate axes, respectively. , , For the speed error of the three axes, , , The first dimension represents the position error along the three axes, while the last six dimensions represent the random walk errors of the gyroscope and accelerometer, respectively.

[0068] An adaptive Kalman filter algorithm is used, and the two parameters that most significantly affect the fusion filtering effect are the environmental noise covariance matrix during state update. The environmental noise covariance matrix during measurement updates By employing a weighted method to increase the weighting coefficients of recent data items, the role of older measurement data in the estimation is weakened, allowing recent measurement data to play a major role in the estimation. This ensures that, even in complex noise conditions, the initial parameters are determined by parameter tuning, and subsequent system parameters mainly depend on the values ​​from nearby time points. The expression can be written as:

[0069]

[0070]

[0071] in, It is a measurement noise matrix. Let b be the initial noise value of the measurement matrix, and b be the iteration basis, which is more suitable to be between 0.9 and 0.99.

[0072] Incompleteness constraints:

[0073] When the vehicle is moving normally, it will not jump off the ground or slip on it; that is, the velocity components in the plane perpendicular to the vehicle's direction of travel are all zero. This direction is also the y-axis in the vehicle's coordinate system. This is a non-holonomic constraint condition for the vehicle. When the angular velocity of the vehicle during a turn is greater than 0.05 rad / s, sideslip may occur. In this case, the non-holonomic constraint condition needs to be disabled. The expression can be written as:

[0074]

[0075] in, , The velocity components of the volume on the y and z axes are downloaded to the carrier coordinate system. Let be the angular velocity of the vehicle on the horizontal plane.

[0076] The two-axis velocities of the carrier coordinate system calculated by the inertial navigation system are used as disturbance velocities to feed back and compensate for the inertial navigation parameters, thereby improving the observability of the system. The measurement matrix of the system is rewritten as follows:

[0077]

[0078] in It is the position difference between the GNSS and INS solutions, and This is the velocity difference between the GNSS and INS solutions. These two disturbance velocities are the solutions of the inertial navigation system on two axes perpendicular to the direction of travel. Dividing the difference between the disturbance velocities in adjacent time intervals by the time interval yields the dynamic bias of the accelerometer, which modifies the parameters of the state variables in the integrated positioning system in real time. This can be expressed as:

[0079]

[0080] in , These are the dynamic offsets of the accelerometer along the z and y axes, respectively. , , , These represent the perturbation velocity values ​​along the z and y axes at adjacent time points. For time intervals.

[0081] Zero-speed detection algorithm:

[0082] A zero-speed detection algorithm is employed to determine when the vehicle is stationary. The observed velocity is set to zero, thus correcting the predicted value output by the INS. In actual driving, vehicles frequently stop, and the zero-speed detection algorithm theoretically effectively suppresses error accumulation in the INS system due to prolonged operation, improving the positioning accuracy of the entire integrated navigation system. A threshold is set; when the detected GNSS velocity or the INS calculated velocity is less than this threshold, the vehicle is considered stationary. Only the difference between the velocity calculated by the INS and the velocity calculated by the GNSS is used as the observed value, and the position and attitude information are averaged over the previous time period to reduce error accumulation in the INS. The threshold is set to 0.03~0.07. This is more suitable; the expression can be written as:

[0083]

[0084] Where Euler_angle represents the carrier's attitude angle, position represents the carrier's position, and the threshold is set to... Time interval Set as .

[0085] Dead reckoning algorithm:

[0086] The dead reckoning algorithm combines CAN velocity information with IMU data to calculate the next position and motion state of the vehicle based on its current location and motion state. The CAN velocity information can be represented as:

[0087]

[0088] in This is the speed reading returned by the CAN bus, which can be converted to the navigation coordinate system using a coordinate transformation matrix:

[0089]

[0090] in For CAN speed information, as an observation of the combined system, coordinate transformation matrix It is derived from inertial navigation calculations.

[0091] The position information output by the odometer can be obtained from the CAN speed information.

[0092]

[0093] in , , This indicates the latitude, longitude, and elevation information obtained via CAN speed. , , The speed readings returned by CAN in the navigation coordinate system are mapped to the speeds of the three axes of the northeast celestial sphere.

[0094] The fusion filter state variables used by the system can be written as:

[0095]

[0096] The first 15 dimensions of the state variables are the three-axis attitude angle error, three-axis velocity error, three-axis position error calculated by the IMU, the random walk of the three axes of the gyroscope, and the random walk of the three axes of the accelerometer. The last three-dimensional variable is the scale error of the three axes of the odometer.

[0097] The state equation of the system is expressed as:

[0098]

[0099] Where the state transition matrix Represented as:

[0100]

[0101] in , , It is the coefficient matrix of the angle, velocity, and position terms in the attitude error equation during inertial navigation system (INS) calculation. , , It is the coefficient matrix of the angle, velocity, and position terms in the velocity error equation. , It is the coefficient matrix of the velocity and position terms in the position error equation.

[0102] System noise figure and system noise matrix Represented as:

[0103]

[0104]

[0105] in , These are the random walk error vectors for the gyroscope and accelerometer, respectively.

[0106] The system measurement equation is expressed as:

[0107]

[0108] The measurement noise matrix Measurement matrix for measuring ambient noise in odometer measurements It can be represented as:

[0109]

[0110] The flowchart of the combined positioning method of GNSS / SINS and vehicle CAN bus speed information of the present invention is as follows: Figure 2 As shown, the specific steps include:

[0111] Step 1: Power on and initialize the module ports;

[0112] Initialize and calibrate the MCU microcontroller, SINS module, GPS module, and CAN data transceiver module, and configure the interrupt controller and UART serial port. The priority of the MCU microcontroller's data transmission and reception serial port needs to be set to ensure that each sensor can receive data in an orderly and effective manner, without data conflicts or missing key data. The priority of the MCU microcontroller receiving GNSS data through the serial port should be set to a high level, so that each incoming GNSS signal can be fused and filtered with the immediately following SINS solution data.

[0113] Step 2: Initial SINS alignment and calculation, and acquisition of GNSS positioning information;

[0114] After the code is burned in and the product is powered on, the MCU will automatically configure the ports; in normal working mode, it reads the inertial element data output by SINS calculation at a frequency of 200Hz and receives navigation and positioning information sent by satellites; and completes the initial alignment of SINS by acquiring continuous GNSS positioning information.

[0115] Step 3: Fusion filtering algorithm;

[0116] When GNSS information is deemed valid, the inertial element data output by the IMU is fused with the GNSS positioning information; when GNSS information is deemed invalid, the speed information output by the on-board diagnostic system (OBD) interface is fused with the IMU's calculated information to obtain the current vehicle's attitude, position, and speed information, and the fused results are transmitted externally via a serial communication protocol.

[0117] The combined GNSS / SINS positioning system with vehicle CAN bus speed information designed in this invention can improve positioning accuracy to a certain extent compared with a single GNSS system. It still has good positioning performance for a certain period of time when GNSS signal is missing. It is low in cost and has low latency, and can achieve convenient communication with the outside world. The addition of an adaptive extended Kalman filter algorithm and the use of vehicle kinematic model for constraints can improve the overall positioning accuracy of the system to a certain extent.

[0118] Table 1 shows a comparison of the magnitude of improvement in system accuracy achieved by adding different algorithms to a single test result. The experimental vehicle was a Volkswagen Tiguan SUV, and the test system included a GNSS antenna placed on the front of the roof, a high-precision Wuhan Kuibu positioning (decimeter position accuracy) receiver used as a reference, and a development board fixed to the vehicle and connected to the vehicle's OBD port. The experimental environment was Tongji University Jiading Campus.

[0119] Table 1. Comparison of the extent to which different algorithms improve system accuracy in a single test result.

[0120]

[0121] As shown in Table 1, for this test data, the zero-velocity detection algorithm significantly improves the system's positioning accuracy. The adaptive Kalman filter algorithm and non-holonomic constraints offer smaller improvements, while adding all three algorithms simultaneously greatly enhances the system's positioning performance.

[0122] Figures 3-5 These are experimental satellite images from the test, showing the ordinary GPS / SINS integrated navigation system under normal conditions and with GPS signal loss, and the system designed in this invention under the same GPS signal loss condition. It can be seen that in the absence of a GPS signal, the positioning results of the ordinary GPS / SINS system diverge rapidly, while the system designed in this invention can maintain its position for a certain period (<30s, 40km / h) without diverging, and the curve is smoother.

[0123] It should be noted that the integrated navigation method disclosed in the embodiments of the present invention is only a preferred embodiment in this field, and is only used to illustrate the technical solution of the present invention and not to limit it; the choice of terms used herein and the explanation of the principles of each embodiment are understandable to other people skilled in the art.

Claims

1. A combined positioning system integrating GNSS / SINS and vehicle CAN bus speed information, characterized in that, Based on MEMS-IMU and GNSS receiver modules, the required inertial navigation solution information and GNSS positioning information are acquired in real time, and the vehicle speed information is obtained through the vehicle's OBD port. Finally, these parameters are processed by PC to obtain the fused positioning result. The integrated positioning system includes: a SINS module, a GPS module, a CAN data transceiver module, and an integrated navigation computing unit; The integrated navigation calculation unit includes an MCU microcontroller that runs the integrated navigation algorithm and completes the calculations for integrated navigation. The integrated navigation algorithm is based on a fusion positioning algorithm using extended Kalman filtering: when the number of satellites received by the satellite signal receiver and the GNSS accuracy coefficient are within a set range, the satellite signal is considered valid; when the satellite signal is valid, the difference between the position and velocity output by the receiver and the position and velocity output by the INS forms the observed measurement of the carrier's position and velocity in the fusion filtering algorithm; when the satellite signal is invalid, the difference between the velocity within the CAN and the output velocity of the INS forms the observed measurement of the carrier's velocity and the filtering result is calculated; an adaptive extended Kalman filtering algorithm is used to adaptively update the environmental noise covariance matrix during state updates. and the environmental noise covariance matrix during measurement updates This allows the two key parameters for updating the extended Kalman filter to adjust autonomously according to different environmental conditions, reducing errors; The integrated navigation algorithm includes a non-holonomic constraint algorithm and a zero-speed detection algorithm. The non-holonomic constraint algorithm restricts the inertial navigation system's (INS) speed updates. Given the frequent stopping situations in actual driving, if the detected GNSS speed or the INS calculated speed is less than this threshold, the vehicle is considered not to have moved. The threshold is set within the range of 0.03 to 0.

07. The zero-velocity detection algorithm uses only the difference between the velocity information calculated by inertial navigation and the velocity information obtained by GNSS as the observation, and takes the average processing value of the position and attitude angle information in the previous time period to reduce the accumulation of errors in inertial navigation. The velocity components in the plane perpendicular to the vehicle's direction of travel are all zero. This direction is also the y-axis in the vehicle's coordinate system; this is a non-holonomic constraint condition for the vehicle. When the vehicle's angular velocity during a turn exceeds 0.05 rad / s, sideslip may occur. In this case, the non-holonomic constraint condition needs to be disabled, and the expression is written as: in, , The velocity components of the volume on the y and z axes are downloaded to the carrier coordinate system. Let be the angular velocity of the vehicle on the horizontal plane; The two-axis velocities of the carrier coordinate system calculated by the inertial navigation system are used as disturbance velocities to feed back and compensate for the inertial navigation parameters, thereby improving the observability of the system. The measurement matrix of the system is rewritten as follows: in It is the position difference between the GNSS and INS solutions, and This is the velocity difference between the GNSS and INS solutions. These two disturbance velocities are the solutions for inertial navigation on two axes perpendicular to the direction of travel. The dynamic bias of the accelerometer is obtained by subtracting the disturbance velocities from adjacent time intervals and dividing by the time interval. This dynamically modifies the parameters of the state variables in the integrated positioning system, expressed as: in , These are the dynamic offsets of the accelerometer along the z and y axes, respectively. , , , These represent the perturbation velocity values ​​along the z and y axes at adjacent time points. For time intervals.

2. The combined positioning system of GNSS / SINS and vehicle CAN bus speed information as described in claim 1, characterized in that, The SINS module includes an inertial measurement unit and a pose calculation module; The inertial measurement unit includes a gyroscope and an accelerometer, wherein The gyroscope measures and acquires the angular velocity information of the carrier, and calculates the coordinate transformation matrix from the carrier coordinate system to the navigation coordinate system at subsequent moments based on the initial angle determined by the initial alignment. The accelerometer measures and acquires acceleration information, and obtains the acceleration components mapped onto the three axes of the navigation coordinate system through a coordinate transformation matrix; The pose calculation module includes a strapdown inertial navigation algorithm module, which calculates the attitude angles, velocity, and position information of the carrier using the angular velocity and acceleration information obtained from the inertial measurement unit. The SINS module can only measure the changes in the attitude angles, velocity, and position of the carrier relative to its initial position. Through the initial alignment of the strapdown inertial navigation, the initial latitude, longitude, and elevation information of the carrier and the angles between the three axes of the carrier coordinate system and the navigation coordinate system are known, thus enabling real-time acquisition of the carrier's navigation and positioning information during subsequent motion. The GPS module includes a GNSS receiver, which integrates an RF chip, a baseband chip, and a processing chip. An external antenna receives and decodes signals emitted by satellites to calculate the three-dimensional coordinates of the observation point. For a moving vehicle, its speed information is deduced from the changes in the three-dimensional coordinate information at continuous time intervals. The CAN data transceiver module includes a CAN transceiver chip. The CAN data transceiver module connects the integrated navigation computing unit and the vehicle OBD port, and connects to the vehicle CAN bus through the vehicle OBD port. The CAN transceiver chip enables communication between the integrated navigation computing unit and the vehicle CAN bus. The integrated navigation computing unit sends commands to the CAN bus and receives data packets containing vehicle speed information via the CAN transceiver chip.

3. The combined positioning system of GNSS / SINS and vehicle CAN bus speed information as described in claim 1, characterized in that, The system workflow includes the following steps: Step 1: Based on the development board with integrated MEMS-IMU and GNSS receiver modules, acquire the required inertial navigation calculation information and GNSS positioning information in real time; Step 2: The development board communicates with the CAN bus via the vehicle's OBD port, sending commands and receiving returned data frames; the development board connects to an external PC wirelessly or via wired connection to transmit and display the positioning results in real time. Step 3: The development board's integrated navigation calculation unit evaluates the validity of the acquired satellite signals based on the number of received satellites, accuracy factor, and GNSS signal status information obtained by the GNSS signal receiving module. When the satellite signals are valid, an adaptive extended Kalman filter fusion algorithm is used to combine the GNSS positioning results with the inertial navigation solution results. When the satellite signals are determined to be invalid, a dead reckoning algorithm is used to fuse the inertial navigation solution results and OBD velocity information to obtain the positioning result. Step 4: Based on the actual movement of the vehicle, non-integrity constraints and vehicle kinematic constraints are used to constrain the parameters in the fusion filtering algorithm to improve the positioning accuracy.

4. The combined positioning system of GNSS / SINS and vehicle CAN bus speed information as described in claim 1, characterized in that, The adaptive extended Kalman filter algorithm is as follows: The adaptive extended Kalman filter algorithm consists of two parts: state update and measurement update. Both GNSS and inertial navigation systems can obtain the vehicle's velocity and position information. The inertial navigation system has a high calculation frequency, while the GNSS signal output frequency is low. Using the inertial navigation calculation part as the basic system, the satellite signal acquired by the satellite receiver is used as a reference for correction to adjust the inertial navigation parameters, resulting in a fusion result. The system state equation and measurement equation are expressed as follows: in, Is The system state variables at time t, and the system observation vector are: , and These are the system noise figure and the system noise matrix, respectively. It is the system measurement matrix. It is a measurement noise matrix. and These are the system state update and the environmental noise during the observation period, respectively. The system state transition matrix is ​​determined by this algorithm. , , , These are the three-axis velocity vectors and three-axis position vectors obtained from INS and GNSS solutions in the navigation coordinate system, respectively; and the system state transition matrix. Represented as: in, , , It is the coefficient matrix of the angle, velocity, and position terms in the attitude error equation during inertial navigation system (INS) calculation. , , It is the coefficient matrix of the angle, velocity, and position terms in the velocity error equation. , It is the coefficient matrix of the velocity and position terms in the position error equation; the system measurement matrix. System noise figure System noise matrix With measurement noise matrix They are represented as follows: in , These are the random walk error vectors for the gyroscope and accelerometer, respectively. , These are the noise vectors for velocity measurement and position measurement at the GNSS receiver, respectively. The Extended Kalman Filter (EKF) model is used to transform problems where nonlinearity is not particularly pronounced into easily manageable linear problems. The EKF is expanded using the Taylor series, and by taking the first-order term, the filtering formula for the nonlinear system state is expressed as: in, Let covariance be the state variable. and These are the system state update and environmental noise covariance during the observation period, respectively. This represents the Kalman filter gain; other variables are the same as above. respectively dimensional state variables Subsequent Taylor expansion; writing the Taylor formula expansion: in express of First derivative, It is the remainder term of Taylor's formula; The system's state variables are 15-dimensional, representing the errors in the carrier's velocity, position, and attitude, as well as the deviations of the accelerometer and gyroscope axes: in , , These are the attitude angle errors calculated by the inertial measurement unit and mapped onto the three coordinate axes, respectively. , , For the speed error of the three axes, , , The first dimension represents the position error along the three axes, while the last six dimensions represent the random walk errors of the gyroscope and accelerometer, respectively. An adaptive Kalman filter algorithm is used, and the two parameters that most significantly affect the fusion filtering effect are the environmental noise covariance matrix during state update. The environmental noise covariance matrix during measurement updates A weighted method is used to increase the weighting coefficient of recent data items, weakening the role of older measurement data in the estimation and allowing recent measurement data to play a major role. This ensures that, under complex noise conditions, the initial parameters are determined by parameter tuning, and subsequent system parameters depend on the values ​​of nearby time points. The expression is written as: in, It is a measurement noise matrix. Let b be the initial noise value of the measurement matrix, and b be the iteration basis quantity, which is 0.9-0.

99.

5. The combined positioning system of GNSS / SINS and vehicle CAN bus speed information as described in claim 1, characterized in that, The zero-speed detection algorithm is specifically as follows: A zero-speed detection algorithm is employed to determine when the vehicle is stationary, setting the observed velocity to zero and correcting the predicted value output by the INS. In actual driving, vehicles frequently stop; the zero-speed detection algorithm suppresses error accumulation in the INS system due to prolonged operation, improving the positioning accuracy of the entire integrated navigation system. A threshold is set; when the detected GNSS velocity or inertial navigation calculated velocity is less than this threshold, the vehicle is considered stationary, and only the difference between the velocity calculated by inertial navigation and the velocity calculated by GNSS is used as the observed quantity. The position and attitude information are averaged over the previous time period to reduce error accumulation in inertial navigation. The threshold is set to 0.03~0.

07. The expression is written as: Where Euler_angle represents the carrier's attitude angle, position represents the carrier's position, and the threshold is set to... Time interval Set as .

6. The combined positioning system of GNSS / SINS and vehicle CAN bus speed information as described in claim 3, characterized in that, The dead reckoning algorithm is as follows: The dead reckoning algorithm combines CAN velocity information with IMU data to calculate the next position and motion state of the vehicle based on its current location and motion state. The CAN velocity information is represented as follows: in This is the speed reading returned by the CAN bus, which can be converted to the navigation coordinate system using a coordinate transformation matrix: in For CAN speed information, as an observation of the combined system, coordinate transformation matrix Derived from inertial navigation calculations; The position information output by the odometer is obtained through CAN speed information: in , , This indicates the latitude, longitude, and elevation information obtained via CAN speed. , , Map the speed readings returned by CAN in the navigation coordinate system to the speeds of the three axes of the northeast celestial sphere; The fusion filtering state variables used by the system are written as follows: The first 15 dimensions of the state variables are the three-axis attitude angle error, three-axis velocity error, three-axis position error calculated by the IMU, the random walk of the three axes of the gyroscope, and the random walk of the three axes of the accelerometer. The last three-dimensional variable is the scale error of the three axes of the odometer. The state equation of the system is expressed as: Where the state transition matrix Represented as: in , , It is the coefficient matrix of the angle, velocity, and position terms in the attitude error equation during inertial navigation system (INS) calculation. , , It is the coefficient matrix of the angle, velocity, and position terms in the velocity error equation. , It is the coefficient matrix of the velocity and position terms in the position error equation; System noise figure and system noise matrix Represented as: in , These are the random walk error vectors of the gyroscope and accelerometer, respectively; The system measurement equation is expressed as: The measurement noise matrix Measurement matrix for measuring ambient noise in odometer measurements Represented as: 。

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