Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 727430, 16 pages.

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

Xianghui Yuan, Feng Lian, and Chongzhao Han

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 727430, 16 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394806103

Digital Object Identifier
doi:10.1155/2013/727430

Zentralblatt MATH identifier
06950840

Citation

Yuan, Xianghui; Lian, Feng; Han, Chongzhao. Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets. J. Appl. Math. 2013, Special Issue (2013), Article ID 727430, 16 pages. doi:10.1155/2013/727430. https://projecteuclid.org/euclid.jam/1394806103


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