Particle probability hypothesis density filter based on pairwise Markov chains

DOI10.3390/A12020031zbMath1461.60064arXiv1811.12211OpenAlexW2902643093WikidataQ128486147 ScholiaQ128486147MaRDI QIDQ2632501

Zheng Li, Chunping Wang, Wei Wang, Jiangyi Liu

Publication date: 14 May 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.12211

zbMATH Keywords

particle filterprobability hypothesis densitypairwise Markov chainmulti-target tracking system

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Markov processes: estimation; hidden Markov models (62M05) Signal detection and filtering (aspects of stochastic processes) (60G35) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

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• Dempster-Shafer fusion in triplet partially Markov chains.
• A Consistent Metric for Performance Evaluation of Multi-Object Filters
• Bayesian Multi-Object Filtering for Pairwise Markov Chains
• The Gaussian Mixture Probability Hypothesis Density Filter
• Signal and Image Segmentation Using Pairwise Markov Chains

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