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Article

Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

Automatic Target Recognition (ATR) Key Laboratory, Shenzhen University, Shenzhen 518060, China
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Sensors 2020, 20(18), 5387; https://doi.org/10.3390/s20185387
Received: 3 August 2020 / Revised: 13 September 2020 / Accepted: 16 September 2020 / Published: 20 September 2020
The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter. View Full-Text
Keywords: extended target tracking; gamma-Gaussian-inverse Wishart; Poisson multi-Bernoulli mixture extended target tracking; gamma-Gaussian-inverse Wishart; Poisson multi-Bernoulli mixture
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MDPI and ACS Style

Du, H.; Xie, W. Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter. Sensors 2020, 20, 5387. https://doi.org/10.3390/s20185387

AMA Style

Du H, Xie W. Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter. Sensors. 2020; 20(18):5387. https://doi.org/10.3390/s20185387

Chicago/Turabian Style

Du, Haocui, and Weixin Xie. 2020. "Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter" Sensors 20, no. 18: 5387. https://doi.org/10.3390/s20185387

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