Journal of Systems Engineering and Electronics ›› 2012, Vol. 23 ›› Issue (3): 364-371.doi: 10.1109/JSEE.2012.00045
• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles
Wei Yang∗, Yaowen Fu, and Xiang Li
Online:
Published:
Abstract:
The finite set statistics provides a mathematically rigorous single target Bayesian filter (STBF) for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded tracking performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Motivated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MMSTBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.
Wei Yang, Yaowen Fu, and Xiang Li. Multiple-model Bayesian filtering with random finite set observation[J]. Journal of Systems Engineering and Electronics, 2012, 23(3): 364-371.
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URL: https://www.jseepub.com/EN/10.1109/JSEE.2012.00045
https://www.jseepub.com/EN/Y2012/V23/I3/364