NDLI: Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches

Content Provider	IEEE Xplore Digital Library
Author	Minvielle, P. Marrs, A.D. Maskell, S. Doucet, A.
Copyright Year	2005
Description	Author affiliation: CEA DAM, France (Minvielle, P.)
Abstract	This paper deals with model-based approaches for joint target tracking and identification. In a Bayesian framework, parametric state-space model classes are introduced as a generalization of the widespread state-space models. In addition to the dynamic state, they include a hyper-parameter, which takes into account target features or behaviors. For such model classes, sequential Monte Carlo approaches, also known as particle filtering, provide a powerful tool to perform sequentially on-line estimation and model selection. The paper focuses on the ergodicity concern of fixed hyper-parameter estimation and model selection. Indeed, the infinite memory of such a system may lead to the particle filter degeneracy or divergence. It reviews various methods to solve this problem, from the common and basic trick of adding an artificial noise to more complex methods, such as the introduction of reversible jump Markov chain Monte Carlo moves.
File Size	1625705
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1591863
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Monte Carlo methods Radar tracking Sensor phenomena and characterization Kinematics Image sensors Power system modeling Filtering Missiles Signal processing MCMC Tracking identification particle filtering parameter estimation ergodicity
Content Type	Text
Resource Type	Article

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