A Vehicle Combination Positioning Algorithm

Abstract

The invention discloses a novel vehicle combination positioning algorithm, and the algorithm employs the technology of federated Kalman filtering data fusion to achieve the information fusion of three positioning subsystems: RSU (road-side unit) positioning, satellite positioning (GNSS) and DR (dead reckoning). In other words, a linear Kalman filter serves as a local filter of an RSU positioning subsystem, and is named as LF1, wherein the corresponding information distribution coefficient is beta1; the GNSS positioning subsystem also employs the linear Kalman filter as the local filter which is named as LF2, wherein the corresponding information distribution coefficient is beta2; and the DR positioning subsystem employs an extended Kalman filter as the local filter which is named as LF3, wherein the corresponding information distribution coefficient is beta3, and a main filter is used for information fusion. Residual error x2 detection and residual hardware detection are employed for building a two-stage fault detection method, and the method can detect a hard fault and a soft fault of the subsystems at the same time. Meanwhile, the algorithm dynamically adjust the information distribution coefficients of the combined system according to positioning precision factors of the positioning subsystems, enables the system to be able to quickly adapt to the environment change, and improves the vehicle positioning precision and reliability.

Description

technical field [0001] The invention relates to the research field of vehicle positioning, in particular to a vehicle combination positioning algorithm. Background technique [0002] Satellite positioning has the characteristics of high precision, low cost, and ease of use. As long as the satellite signal can be received, the satellite positioning receiver can solve the vehicle coordinates, and the error will not accumulate. But in the complex environment of the city, because the satellite signal will be blocked by buildings, the accuracy of satellite positioning will decrease. When the satellite positioning system is used alone, the system positioning reliability is low. Dead reckoning (DR) positioning system is a commonly used vehicle autonomous positioning method. As long as the initial position of the vehicle is given, the current position of the vehicle can be calculated by using the vehicle's driving speed and direction of travel, and it can provide high-precision in ...

AI Technical Summary

Problems solved by technology

The above three systems have their own advantages and disadvantages. Facing the complex urban environment, a single system cannot meet the positioning requirements of the current Internet of Vehicles application. Using information fusion technology to improve the reliability and accuracy of the positioning system will be a feasible solution to vehicle positioning.

[0003] If these three systems use centralized Kalman filtering to achieve information fusion, there will be two problems: first, the computational burden is heavy

When any subsystem fails, if it cannot be detected quickly, the combined positioning system will be polluted by the faulty subsystem, making the output position information unreliable

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