Advances in multi-target filtering of evasive targets
Stefano P. Coraluppi, Craig A. Carthel · 2015
This paper introduces multi-target filtering advances for challenging multi-target tracking scenarios. First, we propose an Interacting Multiple Model (IMM) filter for tracking evasive move-stop-move targets, by exploiting a modified Ornstein Uhlenbeck (OU) process model for target motion. Second, we introduce an asynchronous approach to data association that is applicable to multi-sensor settings where update rates and information content vary greatly across sensors. We validate improved performance using global nearest neighbor (GNN) data association and discuss its applicability to multi-target tracking (MTT) under the MHT paradigm.