Acoustic scattering of underwater targets

The objective of this paper is to provide feature extraction algorithm for underwater targets. The targets are homogeneous elastic bodies of finite dimensions. The targets considered are a brass sphere, a PVC sphere, a brass cylinder, a PVC cylinder, concrete block and MS cylinder of different dimensions. The incident acoustic signal used was a linear frequency modulated (LFM) signal of finite duration with the signal bandwidth of 40 kHz to 80 kHz. The scattered acoustic signal from the targets are recorded and processed for feature selection. The scattered signals were analysed using power spectrum analysis, Linear Predictive Coding and Auto Regressive (AR) modelling, and its statistical features are extracted for all the targets. The nature of the backscattered signal for the underwater targets is also explained. The extracted features are passed into the feed forward neural network (FFNN) classifier. FFNN was used to identify the targets of six classes, to check the validity of extracting the feature of the targets. The result of the neural network shows that this feature extraction algorithm could enhance the fractal features of the signals and reduce the number of dimensions of the feature space and prove that it can efficiently classify underwater targets. A comprehensive study was then carried out to compare the classification performance by using these data sets in terms of performance analysis like specificity and sensitivity.