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• W2112344375 abstract "This paper is concerned with the problem of tracking single or multiple targets with multiple non-target specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization to the nonlinear non-Gaussian case of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the innovation error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking applications." @default.
• W2112344375 created "2016-06-24" @default.
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• W2112344375 creator A5081314418 @default.
• W2112344375 date "2014-04-16" @default.
• W2112344375 modified "2023-09-28" @default.
• W2112344375 title "Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications" @default.
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• W2112344375 hasPublicationYear "2014" @default.
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