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Improved IMM Filter for Tracking a Highly Maneuvering Target with Mixed System Noises

Kangwagye Samuel and Jae Weon Choi*
International Journal of Control, Automation, and Systems, vol. 16, no. 6, pp.2763-2771, 2018

Abstract : We propose a method of improving tracking filter performance of a highly maneuvering target with mixed system noises in this paper. A case study of an off-road high speed moving target is considered. The system noises consist of white Gaussian noises generated from target motion models and additional colored noises arising from the effect of rough and uneven terrain profile. we design the colored noise first order discrete Markov dynamic system representing terrain conditions. Tracking is done by using an IMM filter with discrete white noise acceleration and horizontal coordinated turn models. The designed colored noise dynamic model is augmented with each of the motion models. We use Kalman filter for linear DWNA model while extended and unscented Kalman filters are used for nonlinear HCT model. A test scenario is setup and simulations are carried out. For filter performance comparison purposes, two more cases are considered i.e., systems with white noncorrelated system noises and the system correlated noise cases. Results show that the proposed method outperforms the traditional error treatment methods in terms of robustness, small mean square error, and acceptable computation load and data processing time.

Keyword : Kalman filters, maneuvering target, mixed system noises, off-road ground target, unscented Kalman filter.

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