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• W2128461485 abstract "This paper presents an iterative off-line optimal state estimation algorithm, which yields the maximum a posteriori (MAP) state trajectory estimate of the state sequence of a target maneuvering in clutter. The problem is formulated as a jump Markov linear system and the expectation maximization algorithm is used to compute the state sequence estimate. The proposed algorithm optimally combines a hidden Markov model and a Kalman smoother to yield the MAP target state sequence estimate. The algorithm proposed uses probabilistic multi-hypothesis tracking (PMHT) techniques for tracking a single maneuvering target in clutter. Previous applications of the PMHT technique have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal (in a MAP sense) tracking of a maneuvering target in clutter." @default.
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• W2128461485 date "2002-11-23" @default.
• W2128461485 modified "2023-09-27" @default.
• W2128461485 title "On maneuvering target tracking via the PMHT" @default.
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• W2128461485 doi "https://doi.org/10.1109/cdc.1997.649857" @default.
• W2128461485 hasPublicationYear "2002" @default.
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