Improving anti-submarine warfare tracking performance by incorporating classification information

Prior research has shown that more accurate classification of contact amplitudes can improve tracking performance. In this work, we augment standard, contact-based trackers with a classifier run on features of the received time series from which the contacts were extracted. Ground truth information from benchmark datasets is used in a flexible simulation framework built around the sonar simulation toolset (SST) to generate simulated target and clutter returns. The simulated returns are used as input to the classifier and the contacts from the benchmark datasets as input to the tracker. The classification information provides an additional input to the association step in the probabilistic data association (PDA) and joint PDA trackers, and to the probability of target for each contact in the Bayesian tracker. The results show that even relatively poor classification can make a noticeable improvement in tracking performance. [This work was funded by the U.S. Office of Naval Research, Contract No. N00014-01-G-0460, Delivery Order #36.]