The problem of characterization of active sonar target response has important applications in many fields, including the currently cost-prohibitive recovery of unexploded ordinance on the ocean floor. We present a method for recognizing these objects using a multidisciplinary approach that fuses machine learning, signal processing, and feature engineering. In short, by taking inspiration from other fields, we solve the problem of object recognition in shallow water in an inexpensive way. These techniques add to the body of explored knowledge in the field of active sonar processing and address real-world problems in the process.

Nowadays, fulfillment of the tactical operations in secrecy has great importance for especially subsurface and surface warfare platforms as a result of improvements in weapon technologies. Spreading out of the tactical operations to the larger areas has made discrimination of targets unavoidable. Due to enlargement of the weapon ranges and increasing subtle hostile threats as a result of improving technology, &ldquo / visual&rdquo / target detection methods left the stage to the computerized acoustic signature detection and evaluation methods. Despite this, the research projects have not sufficiently addressed in the field of acoustic signature evaluation. This thesis work mainly investigates classification and recognition techniques with TRN / LOFAR signals, which are emitted from surface and subsurface platforms and proposes possible adaptations of existing methods that may give better results if they are used with these signals. Also a detailed comparison has been made about the experimental results with underwater acoustic signals.