DocumentCode :
3221595
Title :
Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches
Author :
Minvielle, Pierre ; Marrs, Alan D. ; Maskell, Simon ; Doucet, Arnaud
Author_Institution :
CEA DAM, France
Volume :
1
fYear :
2005
fDate :
25-28 July 2005
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.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; radar tracking; state-space methods; target tracking; Bayesian framework; MCMC; ergodicity concern; hyper-parameter estimation; joint target tracking-identification; particle filtering; reversible jump Markov chain Monte Carlo move; sequential Monte Carlo model-based approach; sequential on-line estimation; widespread state-space model; Filtering; Image sensors; Kinematics; Missiles; Monte Carlo methods; Power system modeling; Radar tracking; Sensor phenomena and characterization; Signal processing; Target tracking; MCMC; Tracking; ergodicity; identification; parameter estimation; particle filtering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2005 8th International Conference on
Print_ISBN :
0-7803-9286-8
Type :
conf
DOI :
10.1109/ICIF.2005.1591863
Filename :
1591863
Link To Document :
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