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• W2077969890 abstract "This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion." @default.
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• W2077969890 date "2012-10-01" @default.
• W2077969890 modified "2023-09-30" @default.
• W2077969890 title "Extended Target Tracking using a Gaussian-Mixture PHD Filter" @default.
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• W2077969890 doi "https://doi.org/10.1109/taes.2012.6324703" @default.
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