Bayesian filtering-based multi-vehicle cooperative positioning method

Abstract

The invention provides a Bayesian filtering-based multi-vehicle cooperative positioning method. According to the method, a probability density function for positions a target vehicle and an adjacent vehicle and a probability density function for relative distance between vehicles are used; in each iteration of extended Kalman filtering, a posterior probability estimate of a target vehicle positionis obtained via Bayesian filtering, a final position estimate of the target vehicle is determined according to a maximum posteriori estimate, cooperative positioning is realized via dual filtering operation, vehicle position errors can be reduced, and accuracy of vehicle position information can be improved.

Description

technical field [0001] The invention belongs to the technical field of wireless positioning, and in particular relates to a Bayesian filtering multi-vehicle cooperative positioning method. Background technique [0002] Vehicle location information is a key component of safety-related applications in intelligent transportation systems. Intelligent driving, life services, location services, car services, security protection, and driverless driving, etc., these applications largely rely on the location information of the vehicle. At present, there are many vehicle positioning technologies. The global satellite navigation system (GNSS) ground receiver has low cost and wide satellite coverage, which can provide position and speed information for car users. When the GNSS positioning technology is well covered by satellite signals, it can provide users with business services with a positioning accuracy of about 15 meters, which can basically meet the positioning requirements of cu...

AI Technical Summary

Problems solved by technology

The main idea of ​​the method based on Bayesian filtering is to use the Bayesian method to obtain the posterior probability distribution of the location information from the prior probability distribution, and calculate the location information through the maximum posterior probability estimation, but the method used The prior probability distribution of the vehicle's position information is obtained based on the positioning of the vehicle navigation system. There are certain errors in itself, and the position information of the surrounding vehicles has not been combined. Therefore, the obtained posterior probability distribution has a large error, and the obtained The accuracy of filtering position information is not high

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