Efficient Active Sonar Multitarget Tracking

Abstract

The active sonar environment presents a challenge for any tracking system. Multipath propagation and the fact that targets are, in general, extended objects mean that multiple measurements may be obtained for each target in the surveillance region. In addition, other objects and random scatterers introduce clutter measurements. Whilst pre-processing such as clustering and classification of detections can assist in significantly reducing the number of measurements, there may still be multiple measurements resulting from a single target. This also applies in a multitarget environment, where multiple measurements may be received from each target. Automatic target tracking, which uses measurements in consecutive scans to initiate tracks, generate a large number of false tracks. The key problem for a tracker becomes that of false track discrimination, i.e. confirming true tracks and terminating false tracks. This paper investigates the performance of three trackers for multitarget tracking in the active sonar environment, each using a different data association paradigm. These are the global nearest neighbor (GNN) data association, linear multitarget integrated probabilistic data association (LMIPDA) and nearest neighbour joint probabilistic data association (NNJPDA). A simulation study of these algorithms in a simulated active sonar environment is presented.