Active sonar systems depend on classification algorithms to identify target echoes and suppress false alarms. Historically, classifiers use a set of empirically derived features that exhibit some statistical separation between background clutter and target echoes. In an ideal scenario, these features would form a sufficient set of statistics capturing all of the information required to classify an echo return. Unfortunately, due to their empirically derived nature, features are rarely provably sufficient. To overcome this drawback, a featureless classifier will be presented. Instead of features, the raw data samples, which form a trivial but provable set of sufficient statistics, are used. The adaptive cosine estimate algorithm has a history of success with featureless classification in other applications and is well suited for underwater acoustics. An in-depth look at the featureless algorithm that will include comparisons to several traditional active sonar classifiers will be provided.

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