A Bootstrap-Bayesian Dynamic Modification Model Based on Small Sample Target Features

The classification and recognition of underwater target signal are based on the samples. In the practical field, the amount of available training samples is limited. In this paper, based on analyzing the radiated noise signals of two types of underwater targets, the Bayesian dynamic modification model for underwater acoustic signals is established, which follows the idea of virtual sample generation. According to idea of virtual sample generation, a Bootstrap-Bayesian dynamic modification model for the small-sample-size problem is proposed, based on Bootstrap method and Bayesian parameter estimation. Through the processing of experimental data and comparative analysis of the experimental results, the effectiveness of the feature extraction method and the dynamically modified classification model proposed in this paper are verified. The applicability of the classification model for two types of underwater acoustic signals has also been confirmed.