Track-Oriented MHT for Multiple Maneuvering Targets Using an Optimal Cluster

The problem of multiple-target tracking in the presence of extraneous measurements (i.e., clutter, thermal false alarms and other targets) and no reports (missed target detections) using the MHT (Multiple Hypothesis Tracking) or the JPDA (Joint Probabilistic Data Association) has received a lot of attention in recent years. The MHT requires a heavy computational burden. Therefore, an idea of clusters was introduced in the MHT. Clustering is a process of dividing a large problem in a wide region into independent small problems. A Track-Oriented MHT gives a significant improvement over the conventional method discussed in the original measurement-oriented MHT. Here, a track is defined by a time-series of at most one measurement data from each sample time. The M3 JPDA (Multiple Maneuver Model JPDA) is the extension and improvement of the conventional JPDA. Our computer simulation results showed that the M3 JPDA provides a definite improvement over the JPDA technique with regard to tracking success rates. In this paper, we have shown that the cluster in the Track-Oriented MHT is the finest clustering under the condition that any two tracks which have a common measurement must be in the same cluster. We propose a new technique, M3 Track-Oriented MHT, for maneuvering target tracking in a cluttered environment, using the track-oriented MHT. We can obtain suboptimal techniques in which M3 Track-Oriented MHT is equivalent to the M3 JPDA. This implies good maneuvering multi-target tracking maintenance capability of the M3 Track-Oriented.