Variational Bayesian based adaptive PDA filter in scenarios with unknown detection probability and heavy-tailed process noise

DOI10.1016/J.JFRANKLIN.2021.03.008zbMath1465.93217OpenAlexW3139011884MaRDI QIDQ2030944

Shan He, Panlong Wu, Kai Wu, Xingxiu Li, Peng Yun

Publication date: 8 June 2021

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2021.03.008

zbMATH Keywords

target tracking systemsvariational Bayesian-based adaptive probabilistic data association filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Stochastic learning and adaptive control (93E35)

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Markov chain Monte Carlo based adaptive Rauch-Tung-Striebel smoother

Cites Work

• Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability
• Gaussian mixture CPHD filter with gating technique
• Tracking in a cluttered environment with probabilistic data association
• Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
• Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter
• The Gaussian Mixture Probability Hypothesis Density Filter
• PHD and CPHD Filtering With Unknown Detection Probability
• A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter
• A Novel Kullback–Leibler Divergence Minimization-Based Adaptive Student's t-Filter
• The probabilistic data association filter

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