Transient sonar signal classification using hidden Markov model and neural net

In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension (1-D), and often display an evolutionary pattern over the time scale. The hidden Markov model (HMM) is well-suited to classification of such 1-D signals. Following this intuition, the application of HMM to sonar transient classification is proposed and discussed in this paper. Toward this goal, three different feature vectors based on autoregressive (AR) model, Fourier power spectrum, and wavelet transforms are considered in our work. The neural net (NN) classifier has been successfully used for sonar transient classification. The same set of features as mentioned above is then used with an NN classifier. Some concrete experimental results using “DARPA standard data set I” with HMM and NN classification schemes are presented. Finally, a combined NN/HMM classifier is proposed, and its performance is evaluated with respect to individual classifiers