Group object structure and state estimation with evolving networks and Monte Carlo methods.

Abstract

This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolutional models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. We update both the state and the graph structure based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over a challenging scenario for group motion estimation in urban environments. Results with merging, splitting and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty