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

Miao Yu; Cunjia Liu; Wen-hua Chen; Jonathon Chambers

doi:10.1117/12.2050160

20 June 2014 A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

Miao Yu, Cunjia Liu, Wen-hua Chen, Jonathon Chambers

Author Affiliations +

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 90911I (2014) https://doi.org/10.1117/12.2050160
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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).

Citation Download Citation

Miao Yu, Cunjia Liu, Wen-hua Chen, and Jonathon Chambers "A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911I (20 June 2014); https://doi.org/10.1117/12.2050160

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