Contact clustering and classification using likelihood-based similarities

Abstract

This paper presents the results of using a likelihood-based clustering step before tracking on a multistatic sonar step. The likelihood-based clustering appropriately models the measurement noise and allows for the incorporation of features. The clustering step also allows for the rejection of clutter and fusion of the contact measurements within a cluster. After clustering, fusion and classification, the tracking results are improved over previous preprocessing methods. Results are shown for the three scenarios in the PACSim dataset.