Minimum Hellinger distance classification of passive underwater acoustic signals

Passive source classification in the underwater environment is a challenging problem in part because propagation through the space- and time-varying media introduces variability and uncertainty in the signal. Acoustic propagation codes can predict received fields accurately but they are sensitive to input environmental parameters which cannot be known exactly. This uncertainty in environmental knowledge used in signal predictions results in imperfect statistical class models. Classifiers that rely on simulations of the environment must therefore be robust to imperfect statistical models. Maximum likelihood methods provide ideal performance when the class models are correct but their performance quickly deteriorates when class models are imperfect. Minimum distance methods generally offer robustness to mismatches at the expense of performance, with that trade-off governed by the distance metric used. Several well-studied distance metrics are discussed and it is explained how the Hellinger distance offers robustness to outliers while retaining the performance of a maximum likelihood method, properties that make it well-suited for classification of passive underwater acoustic signals. Examples are provided to quantify the robustness properties of the Hellinger distance when applied to classification of passive underwater acoustic signals and compare its performance to the maximum likelihood method. [Work supported by ONR Undersea Signal Processing.]