Multi-sensor multi-target tracking using out-of-sequence measurements

Out-of-sequence measurements (OOSMS) arise in a multi-sensor central-tracking system due to communication network delays and varying preprocessing times at the sensor platforms. During the last few years a great deal of research has focussed attention on the OOSM filtering problem. However, research in the multi-sensor multi-target OOSM tracking involving data association, filtering, and hypothesis management is still lacking. Some previous efforts have used buffering and measurement reprocessing to handle the OOSMs. In this paper, we present single-model multiple-lag OOSM algorithms for data association, likelihood computation, and hypothesis management for a dwell-based multi-sensor multi-target multi-hypothesis tracking (MHT) system that handles missed detections and clutter. We present numerical results using simulated multi-sensor ground moving target indicator (GMTI) radar measurements.