Transient Sonar Signal Classification Using Hidden Markov Models and Neural Nets
NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA
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In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension 1-D. The hidden Markov model HMM is well suited to classification of 1-D signals such as speech. In HMM methodology, the signal is divided into a sequence of frames, and each frame is represented by a feature vector. This sequence of feature vectors is then modeled by one HMM. Thus, the HMM methodology is highly suitable for classifying the patterns that are made of concatenated sequences of micro patterns. The sonar transient signals often display an evolutionary pattern over the time scale. Following this intuition, the application of HMMs to sonar transient classification is proposed and discussed in this paper.
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