Passive Shallow Water Automated Target Recognition using Deep Convolutional Bi directional Long Short Term Memory

The extremely challenging nature of passive acoustic surveillance makes it a key area of research in NavalNon-Co-operative Target Recognition especially in Anti-Submarine Warfare systems. In shallow waters, thecomplex acoustics due to the highly varying ambient background noise as well as the multi-modal propagation in the surface-bottom bounded channel makes surveillance even difficult. In this work, an ensemble of Convolutional Neural Networks and Bidirectional Long Short Term Memory stages employing soft attention is used to effectively capture the spectro-temporal dynamics of the target signature. In order to alleviate the overall computational cost associated with the optimal model search in the extensive hyperparameter space, a recursive model elimination scheme, making frugal use of the available resources, is also proposed. Experimental analysis on acoustic target records, collected from the shallows of Arabian Sea, has yielded encouraging results in terms of model accuracy, precision and recall