Comparison of effects of sonar bandwidth for underwater target classification

In this paper, two different data sets which use linear FM incident signals with different bandwidths, namely 40 KHz and 80 KHz, are used for benchmarking. The goal is to study the effects of using larger bandwidth for underwater target classification. The classification system is formed of several subsystems including preprocessing, subband decomposition using wavelet packets, linear predictive coding (LPC) in subbands, feature selection and neural network classifier. The classification performance is demonstrated on ten noisy realizations of the data sets formed by adding synthesized reverberation effects with 12 dB signal-to-reverberation ratio. The Receiver Operating Curves (ROC) and the error location plots for these data sets are generated. To compare the generalization and robustness of the system on these data sets, the error and classification rate statistics are generated using Monte Carlo simulations on a large set of noisy data. The results point to the fact; that the wideband sonar provides better robustness property. Three-aspect fusion is also adopted which yields almost perfect classification performance. These issues will be thoroughly studied and analyzed in this paper.