First-moment filters for spatial independent cluster processes

Anthony Swain; Daniel E. Clark

doi:10.1117/12.850034

27 April 2010 First-moment filters for spatial independent cluster processes

Anthony Swain, Daniel E. Clark

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 76970I (2010) https://doi.org/10.1117/12.850034
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

A group target is a collection of individual targets which are, for example, part of a convoy of articulated vehicles or a crowd of football supporters and can be represented mathematically as a spatial cluster process. The process of detecting, tracking and identifying group targets requires the estimation of the evolution of such a dynamic spatial cluster process in time based on a sequence of partial observation sets. A suitable generalisation of the Bayes filter for this system would provide us with an optimal (but computationally intractable) estimate of a multi-group multi-object state based on measurements received up to the current time-step. In this paper, we derive the first-moment approximation of the multi-group multi-target Bayes filter, inspired by the first-moment multi-object Bayes filter derived by Mahler. Such approximations are Bayes optimal and provide estimates for the number of clusters (groups) and their positions in the group state-space, as well as estimates for the number of cluster components (object targets) and their positions in target state-space.

Citation Download Citation

Anthony Swain and Daniel E. Clark "First-moment filters for spatial independent cluster processes", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970I (27 April 2010); https://doi.org/10.1117/12.850034

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