Clustering and Association for Active Sonar Tracking and Classification

Modern active sonar systems often have difficulty meeting submarine detection, classification and tracking objectives due to the well-known complication of high reverberation in littoral operating environments. The conventional signal processing implemented in most Navy active sonar systems is optimized for detecting target echoes in noise. The presence of boundary reverberation provides a highly variable background which reduces detection performance, creates false detections and false objects. In addition, reverberation can impede good clustering by creating false detections which cluster with targets thus adding uncertainty to the cluster parameters. This uncertainty propagates into the tracking process. In-Depth Engineering Corporation (IEC) and Alion Science & Technology (Alion) propose four novel algorithm technologies that address current shortcomings in active sonar processing including improved active detection and clustering.