New environmental dependent modelling with Gaussian particle filtering based implementation for ground vehicle tracking

Yu, M; Xue, YL; Ding, RX; Oh, HD; Chen, WH; Chambers, J

doi:10.1109/SSPD.2016.7590608

New environmental dependent modelling with Gaussian particle filtering based implementation for ground vehicle tracking

Lookup NU author(s): Professor Jonathon Chambers

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Abstract

This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modelling is proposed to reflect the influences of different types of environmental domain knowledge on the vehicle dynamic: i) a non-Markov jump model is applied with multiple models while transition probabilities between models are environmental dependent ii) for a particular model, both the constraints and potential forces obtained from the surrounding environment have been applied to refine the vehicle state distribution. Based on the proposed modelling approach, a Gaussian particle filtering based method is developed to implement the related Bayesian inference for the target state estimation. Simulation studies from multiple Monte Carlo simulations confirm the advantages of the proposed method over traditional ones, from both the modelling and implementation aspects.