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Monte Carlo PHD Filtering

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If you have a question about this talk, please contact Taylan Cemgil .

The Probability Hypothesis Density (PHD) filter approximates the optimal filter for a class of dynamical models in which, at each time, the hidden and observed quantities are spatial point processes. Such models have applications in multi-object tracking, audio processing and communications engineering, where the hidden point-process models a time-varying number of unobserved objects, each of which evolves over time.

Originally formulated in the framework of Finite Set Statistics, the PHD filter has the attractive property that it reduces the dimension of the problem to that of a single unobserved object. However, in many cases of interest, the PHD filtering recursion is analytically intractable. This talk describes recent advances in the use of Monte Carlo methods to approximate the PHD filter.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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