Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets | Zendy

Xianghui Yuan | Zendy; Feng Lian | Zendy; Chongzhao Han | Zendy

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Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

Author(s) -

Xianghui Yuan,

Feng Lian,

Chongzhao Han

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/727430

Subject(s) - clutter , kalman filter , filter (signal processing) , bernoulli's principle , tracking (education) , cardinality (data modeling) , computer science , gaussian , extended kalman filter , control theory (sociology) , algorithm , nonlinear system , monte carlo method , nonlinear filter , mathematics , filter design , artificial intelligence , computer vision , radar , engineering , statistics , data mining , telecommunications , psychology , pedagogy , physics , control (management) , quantum mechanics , aerospace engineering

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter

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