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Activity Number: 308
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303689
Title: Bayes Filtering for Spatio-Temporal Poisson Cluster Point Processes
Author(s): Daniel E. Clark*+ and Anthony Swain
Companies: Heriot-Wat University and Heriot-Wat University
Address: EECE EPS, Edinburgh, International, EH14 4AS, United Kingdom
Keywords: Optimal smoothing ; Bayesian filtering ; Random sets ; Point processes ; sequential Monte Carlo
Abstract:

In the spatio-temporal point process theory community, there is considerable interest in estimating the intensity function of a point process for various applications in epidemiology, forestry, and rainfall modeling. In the aerospace and signal processing communities, Bayesian filtering methods for propagating this intensity function, known as Probability Hypothesis Density (PHD) filters, have had a transformational effect on the development of mathematically principled target tracking algorithms, though these approaches have not received widespread attention in the spatio-temporal point process community. In this paper we derive explicit formulas for the propagating of the intensity function of general cluster point processes that are motivated by applications in target tracking such as force aggregation.

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