Target classification using Renyi entropy features of cyclic bispectrum

Abstract

In this study, a target classification method is proposed based on a third-order cyclic statistics technique. The authors introduce cyclic bispectrum (CBS) to reveal the non-linear cyclic nature contained by the micro-Doppler signal, and it is observed that the non-zero peaks generated by some cyclic non-linear nature form unique distribution patterns on CBS slices for different targets. Then, a Renyi entropy is calculated for each CBS slice to measure the information content and thus achieve an entropy sequence. Subsequently, considering the entropy sequence as a feature vector, the support vector machine classifier is used to perform the target classification. Experimental results based on real measured data validate the effectiveness of the method.