Target classification using Renyi entropy features of cyclic bispectrum
Cai Wang, Yan Li, Meiguo Gao · The Journal of Engineering · 2019
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.