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. © 2010 SPIE.
|Title of host publication||Signal Processing, Sensor Fusion, and Target Recognition XIX|
|Publication status||Published - 2010|
|Event||Advanced Photon Counting Techniques IV - Orlando, FL, United States|
Duration: 7 Apr 2010 → 8 Apr 2010
|Conference||Advanced Photon Counting Techniques IV|
|Period||7/04/10 → 8/04/10|