Histogram PMHT with particles
Date
2011
Authors
Davey, S.
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Conference paper
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Proceedings of the 14th International Conference on Information Fusion (FUSION), 2011 : pp.1-8
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Samuel J. Davey
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International Conference on Information Fusion (14th : 2011 : Chicago, IL)
Abstract
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. Recent comparisons have shown that it can give performance close to numerical approximations to the optimal Bayesian filter at a fraction of the computation cost. The derivation of H-PMHT makes no explicit assumption about the target process model or the sensor point spread function: these details are dictated by the application. However, only linear Gaussian implementations have been used in the literature and there is a growing misconception that H-PMHT requires linear Gaussian models. This paper considers the implementation of H-PMHT for non-linear non-Gaussian problems.
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©2011 IEEE