A performance test for a new reactive-cooperative filter in an ego-vehicle localization application

Abstract

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter is a new robust data fusion approach adapted for ego-vehicle localization in degraded signal conditions. The OKPS is the improved version of the hybridization of the Particle Filter (PF) by Particle Swarm Optimization notions (PSO). Taking also some features from the Extended Kalman filter (EKF), the OKPS is designed for being more robust to noises such as GPS multipaths and also more reactive. The OKPS has the challenge of merging reactivity and resistance to noises. For high dynamic on-road vehicles localization, the balance between reactivity and robustness is critical. This paper introduces an intelligent collaborative localization algorithm inspired by PSO techniques that addresses this challenge. The OKPS filter outline integrates Particle Filter (PF) tracking, PSO evolutionary optimization and EKF self-diagnose. Using real world data, the OKPS is tested in comparison to the EKF and PF approaches performances. The comparison is done following new specific criteria, designed for ego-localization filter performances analysis. Competitive results are reached for a high dynamic on-road vehicle localization application.