A Bayesian framework with auxiliary particle filter for GMTI based ground vehicle tracking aided by domain knowledge

Yu, Miao and Liu, Cunjia and Chambers, Jonathan (2014) A Bayesian framework with auxiliary particle filter for GMTI based ground vehicle tracking aided by domain knowledge. In: Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, SPIE, May 05, 2014, Baltimore, Maryland, USA.

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Item Type:Conference or Workshop contribution (Paper)
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Abstract

In this work, we propose a new ground moving target indicator (GMTI) radar based ground vehicle tracking method which exploits domain knowledge. Multiple state models are considered and a Monte-Carlo sampling based algorithm is preferred due to the manoeuvring of the ground vehicle and the non-linearity of the GMTI measurement model. Unlike the commonly used algorithms such as the interacting multiple model particle filter (IMMPF) and bootstrap multiple model particle filter (BS-MMPF), we propose a new algorithm integrating the more efficient auxiliary particle filter (APF) into a Bayesian framework. Moreover, since the movement of the ground vehicle is likely to be constrained by the road, this information is taken as the domain knowledge and applied together with the tracking algorithm for improving the tracking performance. Simulations are presented to show the advantages of both the new algorithm and incorporation of the road information by evaluating the root mean square error (RMSE).

Keywords:tracking, auxiliary particle filtering, domain knowledge
Subjects:G Mathematical and Computer Sciences > G720 Knowledge Representation
G Mathematical and Computer Sciences > G340 Statistical Modelling
Divisions:College of Science > School of Computer Science
ID Code:26786
Deposited On:31 Mar 2017 08:04

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