Classification of wideband transient signals using spectral-based techniques

Spectral-based classification schemes designed to separate various wideband transient signals are considered and their performances compared to those obtained using a back-propagation neural network implementation. Spectral-based measures considered include the Bhattacharyya distance, the divergence, the normalized cross-correlation coefficient, and the modified normalized cross-correlation coefficient. Results show that accurate classification may be obtained using spectral-based measures and that the performances compare, or are sometimes better, to those obtained using neural networks when the training data used to train the neural network is small. In addition, the spectral-based measures are simple and computationally inexpensive