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- W2079050227 abstract "Sensor management in support of Level 1 data fusion (multisensor integration), or Level 2 data fusion (situation assessment) requires a computationally tractable multitarget filter. The theoretically optimal approach to this multi-target filtering is a suitable generalization of the recursive Bayes nonlinear filter. However, this optimal filter is intractable and computationally challenging that it must usually be approximated. We report on the approximation of a multi-target non-linear filtering for Sensor Management that is based on the particle filter implementation of Stein-Winter probability hypothesis densities (PHDs). Our main focus is on the operational utility of the implementation, and its computational efficiency and robustness for sensor management applications. We present a multitarget Particle Filter (PF) implementation of the PHD that include clustering, regularization, and computational efficiency. We present some open problems, and suggest future developments. Sensor management demonstrations using a simulated multi-target scenario are presented." @default.
- W2079050227 created "2016-06-24" @default.
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- W2079050227 date "2006-05-05" @default.
- W2079050227 modified "2023-09-23" @default.
- W2079050227 title " Regularized multitarget particle filter for sensor management " @default.
- W2079050227 doi "https://doi.org/10.1117/12.666128" @default.
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