New environmental dependent modeling with Gaussian particle filtering based implementation for ground vehicle tracking.PDF (299.85 kB)
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New environmental dependent modeling with Gaussian particle filtering based implementation for ground vehicle tracking

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conference contribution
posted on 05.10.2016, 10:52 authored by Miao Yu, Yali Xue, Runxiao Ding, Hyondong Oh, Wen-Hua ChenWen-Hua Chen, Jonathon Chambers
This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modeling 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 modeling 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 modeling and implementation aspects.

Funding

This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) and the Ministry of Defence (MOD) University Defence Research Collaboration in Signal Processing under the grant number EP/K014307/1.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Sensor Signal Processing for Defence

Citation

YU, M. ... et al, 2016. New environmental dependent modeling with Gaussian particle filtering based implementation for ground vehicle tracking. IN: Sensor Signal Processing for Defence (SSPD), Edinburgh, 22nd-23rd September 2016, pp. 1-5.

Publisher

© Crown. Published by IEEE

Version

AM (Accepted Manuscript)

Acceptance date

23/06/2016

Publication date

2016

Notes

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781509003273

Language

en

Location

Edinburgh

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