A combined positioning method and system based on real-time slip rate estimation and compensation

Abstract

This invention relates to a combined positioning method and system based on real-time slip ratio estimation and compensation. During GNSS availability, the method uses GNSS position and velocity information as measurement information. During GNSS interruption, wheel speed correction and compensation are performed. First, based on the yaw rate information measured by the IMU, a Kalman filter is designed to calculate the longitudinal acceleration of each wheel. Second, the slip ratio of each wheel is estimated according to the slip ratio recursive formula under both driving and braking conditions. Finally, the wheel speed information of the WSS is corrected based on the estimated slip ratio to obtain the compensated wheel speed information as measurement information. Using this measurement information, an error-state Kalman filter is used for measurement updates to achieve vehicle positioning. Compared with existing technologies, this invention has advantages such as improved wheel speed information accuracy, improved positioning accuracy, and enhanced positioning reliability.

Description

Technical Field

[0001] This invention relates to the field of automotive positioning technology, and in particular to a combined positioning method and system based on real-time slip ratio estimation and compensation. Background Technology

[0002] The motion state of intelligent vehicles, including position, speed, and attitude, is crucial information for achieving intelligent driving. Wheel speed sensors (WSS) are an attractive solution for improving the positioning accuracy of GNSS / INS integrated systems during GNSS outages. However, under highly dynamic driving conditions, longitudinal slippage of the wheels leads to poor positioning performance.

[0003] Most studies directly use measured wheel speed information as measurement data, without considering the wheel speed v during vehicle slippage. WSS With actual vehicle speed v vehicle There are errors between them. Especially under strong driving / forced driving and other large dynamic conditions, there are significant errors.

[0004] Chinese patent application CN113985466A discloses a pattern recognition-based integrated navigation method and system. This method updates the vehicle's motion state using a strapdown inertial navigation system (INS) update algorithm; it detects the vehicle's GNSS signal in real time; if the GNSS signal is detected, it selects an integrated navigation model and observations based on the GNSS signal, vehicle speed data from the controller area network, and the three-axis angular velocity output by the inertial measurement unit (INS); and then uses Kalman filtering to predict and update the vehicle's motion state based on the selected integrated navigation model and observations. However, this method does not consider the error between wheel speed and actual vehicle speed during slippage, nor does it consider the impact of slip ratio on positioning, resulting in insufficient positioning accuracy. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art by providing a combined positioning method and system based on real-time slip rate estimation and compensation.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to one aspect of the present invention, a combined positioning method based on real-time slip rate estimation and compensation is provided. This method utilizes a combination of IMU, WSS, and GNSS for positioning, achieves state estimation based on error state Kalman filtering, and estimates and compensates for slip rate when GNSS is unavailable. The method steps include:

[0008] S1. During GNSS availability, the position and velocity information of GNSS are used as measurement information; during GNSS interruption, wheel speed slip ratio correction compensation is performed to obtain the compensated wheel speed information as measurement information.

[0009] S2. Using the measurement information, an error state Kalman filter is used to update the measurement in order to achieve vehicle positioning.

[0010] As a preferred technical solution, the slip ratio correction and compensation steps for wheel speed in S1 are as follows:

[0011] S11. Collect wheel speed information and yaw rate information;

[0012] S12. Use a Kalman filter to obtain the first derivative of the yaw rate;

[0013] S13. Use the first derivative of the yaw rate to obtain the longitudinal acceleration at each wheel.

[0014] S14. Obtain the slip ratio recursive formula under both vehicle driving and braking conditions, and substitute the longitudinal acceleration at each wheel to obtain the slip ratio of each wheel.

[0015] S15. Use the obtained wheel slip ratios to compensate and correct the collected wheel speed information.

[0016] As a preferred technical solution, in S11, the wheel speed information is collected by using WSS to obtain the longitudinal velocity measurement information of the vehicle, thereby obtaining the wheel speed information v in the vehicle coordinate system. WSS .

[0017] As a preferred technical solution, the specific steps for obtaining the first derivative of the yaw rate using the Kalman filter in S12 are as follows: At time t, perform a polynomial Taylor expansion on the yaw rate, ignoring higher-order dynamics (second order and above). The formula is:

[0018]

[0019] Where, ω bz This refers to the yaw rate; The first derivative of the yaw rate; ξ1, ξ2, and ξ3 are the second derivatives of the yaw rate; Δt is the time interval between time k and time k-1; ξ1, ξ2, and ξ3 are the random noise of each term.

[0020] As a preferred technical solution, the recursive formula for the slip ratio under driving conditions of vehicle S14 is as follows:

[0021]

[0022] The recursive formula for the slip ratio under vehicle braking conditions is:

[0023]

[0024] Among them, v wSs,k-1 v is the wheel speed at time k-1; WsS,k Let a be the wheel speed at time k; y,k-1 Δt represents the longitudinal acceleration at time k-1; Δt is the time interval between time k and time k-1.

[0025] As a preferred technical solution, the error state Kalman filter in S2 is specifically based on the state variable X. k The designed state variable X k Includes: position error δp k Speed ​​error Attitude error Accelerometer zero bias and the zero bias of the gyroscope

[0026] As a preferred technical solution, the state equation of the error state Kalman filter in S2 is:

[0027]

[0028] in, Predict the state at time k for time k-1; F is the state transition matrix; The optimal state at time k-1; w k This is the noise matrix.

[0029] As a preferred technical solution, in S2, for measurement information during GNSS availability, the Kalman filter processes the measurement information using the measurement equation as follows:

[0030]

[0031] Where, η k,GNSS For GNSS measurement noise; Z k,GNSS P represents the GNSS observation information at time k; INS The predicted location status is from INS; P GNSS Position measurement information from GNSS; The predicted velocity status is from INS; Velocity measurement information from GNSS; Hk,GNSS Let be the measurement matrix of GNSS information at time k.

[0032] As a preferred technical solution, in S2, for measurement information processed by the Kalman filter during GNSS interruptions, the specific measurement equation is as follows:

[0033]

[0034] H k,WSS =[0 3×3 I 3×3 0 3×9 ];

[0035]

[0036] Where, η k,WSS Measurement noise for WSS; Z k,WSS The observation information of WSS at time k; Speed ​​measurement information from WSS; H k,WSS The measurement matrix of WSS information at time k; Represents the rotation matrix from the b-system to the n-system; It is the rotation matrix from the v-frame to the b-frame; It is wheel speed information after slip ratio compensation.

[0037] According to another aspect of the present invention, a combined positioning system based on real-time slip rate estimation and compensation is provided, characterized in that the system operates using a combined positioning method based on real-time slip rate estimation and compensation as described above, and the system comprises a GNSS module, an INS module, a WSS module and a Kalman filter module;

[0038] The GNSS module is used to acquire vehicle location and speed information;

[0039] The INS module includes an inertial measurement unit (IMU) for acquiring information such as vehicle acceleration and yaw rate.

[0040] The WSS module is used to acquire vehicle wheel speed information and estimate and compensate for its slip ratio.

[0041] The Kalman filter module is used to process measurement information to locate the vehicle.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] 1. The present invention provides a combined positioning method based on real-time slip rate estimation and compensation, which utilizes IMU, WSS and GNSS for combined positioning, achieves state estimation based on error state Kalman filtering, and performs slip rate estimation and compensation when GNSS is unavailable, thereby improving positioning accuracy during GNSS interruption and enhancing positioning accuracy.

[0044] 2. This invention designs an error-state-based Kalman filter. During GNSS availability, GNSS position and velocity information are used as measurement information. During GNSS interruption, wheel speed information is corrected and compensated for slip ratio to obtain compensated wheel speed information as measurement information. Finally, using the measurement information, an error-state Kalman filter is employed for measurement updates to achieve vehicle positioning. This ensures the positioning system remains reliable even during high-dynamic driving, improving positioning reliability.

[0045] 3. In this invention, wheel speed and yaw rate information are collected, and the first derivative of the yaw rate is obtained using a Kalman filter. The longitudinal acceleration at each wheel is then calculated using the first derivative of the yaw rate. A recursive formula for the slip ratio is derived under both driving and braking conditions, and the longitudinal acceleration at each wheel is substituted to obtain the slip ratio for each wheel. The obtained wheel slip ratios are then used to compensate and correct the collected wheel speed information. This improves the accuracy of the wheel speed information, thereby enhancing the vehicle's positioning accuracy. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the architecture of a combined positioning method based on real-time slip rate estimation and compensation in this invention;

[0047] Figure 2 This is a schematic diagram of the experimental vehicle and sensors used for verification in the embodiment.

[0048] Figure 3 This is a schematic diagram of the vehicle speed during straight-line driving in the embodiment.

[0049] Figure 4 This is a diagram of the experimental trajectory during the straight-line driving condition in the embodiment.

[0050] Figure 5 This is a schematic diagram of the longitudinal acceleration estimation results during the straight-line driving condition in the embodiment;

[0051] Figure 6a This is a schematic diagram showing the estimated slip ratio of the left rear wheel during straight-line driving in the embodiment.

[0052] Figure 6b This is a schematic diagram of the right rear wheel slip ratio estimation result under the straight driving condition in the embodiment.

[0053] Figure 7 This is a schematic diagram of the horizontal position error during straight-line driving in the embodiment.

[0054] Figure 8 This is a schematic diagram of the vehicle speed during the turning condition in the embodiment;

[0055] Figure 9 This is a diagram of the experimental trajectory during the turning condition in the embodiment;

[0056] Figure 10 This is a schematic diagram of the longitudinal acceleration estimation results during the turning condition in the embodiment;

[0057] Figure 11a This is a schematic diagram showing the estimated slip ratio of the left rear wheel during the turning condition in the embodiment.

[0058] Figure 11b This is a schematic diagram showing the estimated slip ratio of the right rear wheel during the turning condition in the embodiment.

[0059] Figure 12 This is a schematic diagram of the horizontal position error during the turning condition in the embodiment. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0061] Example

[0062] In this embodiment, in order to obtain high-precision vehicle state estimation, especially during Global Navigation Satellite System (GNSS) outages, applications such as... Figure 1The method described above takes measurements from an Inertial Navigation System (INS), a Wheel Speed ​​Sensor (WSS), and GNSS as inputs, and achieves optimal state estimation based on error-state Kalman filtering. First, recursive state estimation is achieved using measurements from the Inertial Measurement Unit (IMU) based on the INS algorithm. Then, during GNSS availability, state updates are performed using high-precision GNSS measurements. During the more critical period of GNSS interruption, state updates are performed using vehicle wheel speed information. To improve the accuracy of wheel speed measurement under strong driving / braking conditions, an innovative slip ratio estimation and compensation method is proposed. This significantly improves the accuracy of wheel speed information, thereby enhancing vehicle positioning accuracy.

[0063] In this embodiment, the coordinate system used is as follows: (·) n This represents the navigation coordinate system, an n-system, also known as ENU (East-North-Up). The origin coincides with the center of the IMU sensor frame. The vehicle body coordinate system uses (·). b This indicates that it belongs to the b series, and its origin coincides with the center of the IMU. (·) v This represents the vehicle coordinate system, with its origin located at the center of the vehicle's rear axle.

[0064] In this embodiment, a combined positioning method based on real-time slip ratio estimation and compensation is used to locate the vehicle.

[0065] In this embodiment, at time k, the vehicle state X estimated in this paper is... k Includes: position error δp k (Longitude, latitude, and altitude), speed error Attitude error Accelerometer zero bias and the zero bias of the gyroscope

[0066]

[0067] In this embodiment, the INS predicted state and measurement results are fused using an error-state Kalman filter. The state equation of the Kalman filter can be expressed as:

[0068]

[0069] Where F is the state transition matrix; w k Represents the noise matrix; Predict the state at time k for time k-1; This is the optimal state at time k-1.

[0070] In this embodiment, during GNSS availability, the position and speed information of GNSS are used as measurement information; during GNSS interruption, wheel speed slip ratio correction compensation is performed to obtain compensated wheel speed information as measurement information.

[0071] Most studies treat measured wheel speed information directly as a measurement, without considering the wheel speed v during vehicle slippage. WSS With actual vehicle speed v vehicle There are errors between them. Especially under strong driving / forced driving and other large dynamic conditions, there are significant errors.

[0072] In this embodiment, a real-time slip ratio estimation and compensation algorithm is proposed to provide more accurate wheel speed observation.

[0073] In this embodiment, a wheel speed sensor is used to measure the longitudinal speed of the vehicle. Therefore, the wheel speed information obtained in the vehicle coordinate system is v. WSS =[0v WSS 0] T .

[0074] In this embodiment, a Kalman filter is designed to estimate the yaw rate. Performing a polynomial Taylor expansion on the yaw rate at time t, ignoring higher-order dynamics (second order and above), we have:

[0075]

[0076] Where, ω bz This refers to the yaw rate; The first derivative of the yaw rate; ξ1, ξ2, and ξ3 are the second derivatives of the yaw rate; Δt is the time interval between time k and time k-1; ξ1, ξ2, and ξ3 are the random noise of each term.

[0077] In this embodiment, the state equation constructed based on the above formula is as follows:

[0078]

[0079] In this embodiment, the measurement information is ω bz Therefore, the observation equation is defined as follows:

[0080]

[0081] Where, ω bz This refers to the yaw rate; The first derivative of the yaw rate; Let be the second derivative of the yaw rate.

[0082] In this embodiment, by substituting the known quantities, the first derivative of the yaw rate can be obtained.

[0083] In this embodiment, the longitudinal acceleration 'a' at the wheel is required when estimating the slip ratio. y,k-1 Information. When the vehicle is traveling in a straight line, due to the small yaw rate, the vehicle body can be considered a rigid body, and the longitudinal velocity of each part is the same. At this time, the longitudinal acceleration at both the left and right wheels uses the longitudinal acceleration information a from the IMU. b,y However, during cornering, due to the significant difference in speed between the left and right wheels, it is necessary to estimate the longitudinal acceleration at each wheel.

[0084] In this embodiment, the longitudinal velocity at the IMU and the longitudinal velocity at the left wheel have the following conversion relationship:

[0085] v RL,y =v b,y -B L ω bz ;

[0086] In this embodiment, the longitudinal velocity at the IMU and the longitudinal velocity at the right wheel have the following conversion relationship:

[0087] v RR,y =v b,y +B R ω bz ;

[0088] In this embodiment, by differentiating the above equation, we can obtain the estimation formulas for the longitudinal acceleration at the left and right wheels respectively:

[0089]

[0090] Among them, B L and B R These are the wheelbases from the IMU to the left and right wheels, respectively; a b,y Longitudinal acceleration measured by the IMU; ω bz Yaw rate measured by the IMU; It is the first derivative of the yaw rate at the IMU.

[0091] In this embodiment, when the vehicle is driven, the slip ratio of a certain wheel is:

[0092]

[0093] In this embodiment, when the vehicle brakes, the slip ratio of a certain wheel is:

[0094]

[0095] Among them, v vehicle This represents the actual vehicle speed; v WSS This is wheel speed information.

[0096] Combining the kinematic formula v vehicle,k =v vehicle,k-1 +a y,k From Δt, we can derive the recursive formula for the slip ratio under driving acceleration conditions:

[0097]

[0098] In this embodiment, the slip ratio recursive formula under braking conditions is as follows:

[0099]

[0100] Among them, v WSS,k-1 v is the wheel speed at time k-1; WSS,k Let a be the wheel speed at time k; y,k-1 Δt represents the longitudinal acceleration at time k-1; Δt is the time interval between time k and time k-1.

[0101] In this embodiment, the wheel speed information is compensated and corrected based on the estimated slip ratio:

[0102] In this embodiment, when the vehicle accelerates,

[0103]

[0104] In this embodiment, when the vehicle brakes,

[0105]

[0106] in, This is the wheel speed information after slip ratio compensation; s a The wheel slip ratio when the vehicle is in motion; s b This refers to the wheel slip ratio when the vehicle is braking.

[0107] In this embodiment, measurement information is used to update the measurement using an error state Kalman filter in order to achieve vehicle positioning.

[0108] In this embodiment, the measurement equation of the Kalman filter changes according to the measurement information.

[0109] In this embodiment, for measurement information during GNSS availability, the Kalman filter processes the measurement information using the following measurement equation:

[0110]

[0111] Where, η k,GNSS For GNSS measurement noise, Z k,GNSS P represents the GNSS observation information at time k; INS The predicted location status is from INS; P GNSSPosition measurement information from GNSS; The predicted velocity status is from INS; Velocity measurement information from GNSS; H k,GNSS Let be the measurement matrix of GNSS information at time k.

[0112] In this embodiment, for measurement information during GNSS interruption, the measurement equation for processing the measurement information by the Kalman filter is specifically as follows:

[0113]

[0114] H k,WSS =[0 3×3 I 3×3 0 3×9 ];

[0115]

[0116] Where, η k,WSS Measurement noise for WSS; Z k,WSS The observation information of WSS at time k; Speed ​​measurement information from WSS; H k,WSS The measurement matrix of WSS information at time k; This represents the rotation matrix from the b-system to the n-system, which is obtained from the INS system. It is the rotation matrix from the v system to the b system, and it is a pre-calibrated extrinsic parameter matrix. This is the wheel speed information after slip ratio compensation.

[0117] In this embodiment, it is evident from the formula analysis that under highly dynamic conditions such as strong drive or forced braking, the large slip ratio leads to a significant difference between the wheel speed information and the actual vehicle speed. Ignoring this issue would result in substantial positioning errors. However, the slip ratio estimation and compensation method applied in this embodiment effectively solves this problem.

[0118] In this embodiment, a combined positioning method based on real-time slip rate estimation and compensation is verified under actual conditions.

[0119] In this embodiment, the verification experiment utilizes, for example, Figure 2 The vehicle shown is a rear-wheel drive vehicle. The sensors equipped on the vehicle include: a MEMS-IMU ASM330LHH providing acceleration and angular velocity measurements along three axes; a GNSS receiver Novatel 718D providing RTK GNSS measurement information; and excellent GNSS signal accuracy down to the centimeter level.

[0120] In this embodiment, wheel speed data is obtained from the vehicle's controller area network bus interface. Furthermore, the high-precision INS / GNSS integrated positioning system Novatel SPAN KVH 1750 provides high-precision vehicle status as ground truth.

[0121] In this embodiment, Tongji University's Jiading Campus was selected as the experimental site. The experiments included braking conditions during straight-line driving and driving and braking conditions during turning. All experiments were conducted in an open environment, and the GNSS was continuously in Real-Time Kinematic (RTK) mode.

[0122] In this embodiment, the GNSS interruption is simulated by artificially cutting off the GNSS signal in order to verify the effectiveness of the positioning algorithm that takes slip rate compensation into account.

[0123] In this embodiment, the vehicle trajectory during the straight-line driving test is as follows: Figure 4 As shown, blue represents the experimental trajectory of the true value, red represents the experimental trajectory without compensation, and orange represents the experimental trajectory after compensating for slip ratio. The experiment included stages of stationary motion, acceleration, and braking. We simulated a GNSS outage by cutting off the GNSS signal starting at 50 seconds. At this time, the vehicle was braking, with a maximum braking acceleration of 5 m / s². 2 This means that the wheels will slip.

[0124] In this embodiment, the vehicle speed during straight-line driving is as follows: Figure 3 As shown; the longitudinal acceleration estimation results are as follows Figure 5 As shown; blue represents the longitudinal acceleration collected by the IMU, red represents the longitudinal acceleration collected by the Novatel SPAN KVH 1750, and orange represents the longitudinal acceleration estimation result obtained by the method in this embodiment; the estimation results of the slip ratio of the left and right rear wheels under straight driving conditions are as follows. Figure 6a , 6b As shown, the blue line represents the true reference value, and the red line represents the value recursively calculated by this method. It can be seen that the slip ratio estimation accuracy is high; the horizontal position error is as follows... Figure 7 As shown, the blue line represents the horizontal position error without slip ratio compensation, and the red line represents the horizontal position error after slip ratio compensation. It is easy to see that the positioning accuracy is significantly improved after slip ratio compensation is added.

[0125] In this embodiment, the vehicle trajectory during the turning test is as follows: Figure 9As shown, blue represents the experimental trajectory of the true value, red represents the experimental trajectory without compensation, and orange represents the experimental trajectory after compensating for slip ratio. The experiment simulated a GNSS interruption by cutting off the GNSS signal 80 seconds after the vehicle began turning. During this time, the vehicle experienced intense driving and braking processes, which meant that the wheels would slip.

[0126] In this embodiment, the vehicle speed during the turning condition is as follows: Figure 8 As shown; the longitudinal acceleration estimation results are as follows Figure 10 As shown; blue represents the longitudinal acceleration collected by the IMU, red represents the longitudinal acceleration collected by the Novatel SPAN KVH 1750, and orange represents the longitudinal acceleration estimation result obtained by the method in this embodiment; the estimation results of the slip ratio of the left and right rear wheels under straight driving conditions are as follows. Figure 11a , 11b As shown, the blue line represents the true reference value, and the red line represents the value recursively calculated by this method. It can be seen that the slip ratio estimation accuracy is high; the horizontal position error is as follows... Figure 12 As shown, the blue line represents the horizontal position error without slip ratio compensation, and the red line represents the horizontal position error after slip ratio compensation. It is easy to see that the positioning accuracy is significantly improved after slip ratio compensation is added.

[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A combined positioning method based on real-time slip rate estimation and compensation, characterized in that, This method utilizes a combination of INS, WSS, and GNSS for positioning, achieves state estimation based on error state Kalman filtering, and estimates and compensates for slip rate when GNSS is unavailable. The method steps include: S1. During GNSS availability, the position and velocity information of GNSS are used as measurement information; during GNSS interruption, wheel speed slip ratio correction compensation is performed to obtain the compensated wheel speed information as measurement information. S2. Using measurement information, an error state Kalman filter is used to update the measurement in order to achieve vehicle positioning; The steps for correcting and compensating for wheel speed slip ratio in S1 are as follows: S11. Collect wheel speed information and yaw rate information; S12. Use a Kalman filter to obtain the first derivative of the yaw rate; S13. Use the first derivative of the yaw rate to obtain the longitudinal acceleration at each wheel. S14. Obtain the slip ratio recursive formula under both vehicle driving and braking conditions, and substitute the longitudinal acceleration at each wheel to obtain the slip ratio of each wheel. S15. Use the obtained wheel slip ratios to compensate and correct the collected wheel speed information; The recursive formula for the slip ratio under the driving condition of vehicle S14 is as follows: ； The recursive formula for the slip ratio under vehicle braking conditions is: ； in, for Wheel speed at all times; for Wheel speed at all times; for longitudinal acceleration at any moment; for Time and The time interval between moments.

2. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, Specifically, in S11, wheel speed information is acquired by using WSS to obtain longitudinal velocity measurement information of the vehicle, thereby obtaining wheel speed information in the vehicle coordinate system. .

3. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, The specific steps for obtaining the first derivative of the yaw rate using the Kalman filter in S12 are as follows: Performing a polynomial Taylor expansion on the yaw rate at all times, neglecting higher-order dynamics (second order and above), the formula is: ； in, This refers to the yaw rate; The first derivative of the yaw rate; The second derivative of the yaw rate; for Time and The time interval between moments; , and For each order of terms, there is random noise.

4. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, The error state Kalman filter in S2 is specifically based on the state quantity. The designed state variables Including: positional error Speed ​​error Attitude error accelerometer zero bias and the zero bias of the gyroscope .

5. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, The state equation of the error state Kalman filter in S2 is: ； in, Predict the state at time k for time k-1; F is the state transition matrix; This is the optimal state at time k-1; This is the noise matrix.

6. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, The measurement equation used in S2 for updating measurements during GNSS availability, employing an error-state Kalman filter, is specifically as follows: ； ； in, For GNSS measurement noise; The GNSS observation information at time k; The predicted location status is from INS; Position measurement information from GNSS; The predicted velocity status is from INS; Velocity measurement information from GNSS; Let be the measurement matrix of GNSS information at time k.

7. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that, The measurement equation used in S2 for updating measurements during GNSS outages using an error-state Kalman filter is as follows: ； ； ； in, Measurement noise for WSS; The observation information of WSS at time k; Speed ​​measurement information from WSS; The measurement matrix of WSS information at time k; Represents the rotation matrix from the b-system to the n-system; It is the rotation matrix from the v-frame to the b-frame; It is the wheel speed information after slip ratio compensation.

8. A combined positioning system based on real-time slip rate estimation and compensation, characterized in that, The system operates using a combined positioning method based on real-time slip rate estimation and compensation as described in any one of claims 1-7. The system includes a GNSS module, an INS module, a WSS module, and a Kalman filter module. The GNSS module is used to acquire the vehicle's location and speed information; The INS module includes an inertial measurement unit (IMU) for acquiring information including vehicle acceleration and yaw rate. The WSS module is used to acquire vehicle wheel speed information and estimate and compensate for its slip ratio. The Kalman filter module is used to process measurement information to locate the vehicle.

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