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).
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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 (June 20, 2014); doi:10.1117/12.2050160; http://dx.doi.org/10.1117/12.2050160