Classification of RCS sequences based on KL divergence

Abstract

Radar cross section (RCS) is an important characteristic of radar targets. The mean, variance, skewness, kurtosis, varying patterns of RCS sequences provide rich features for radar target classification. In this study, an RCS classification method based on Kullback–Leibler divergence (KL divergence) is proposed. KL divergence is a measurement of the distance between two probabilistic distributions p(x) and q(x). In this method, the discrete probability distributions of the training RCS sequences and their varying patterns are firstly calculated and saved. For a target with unknown label, the corresponding probability distributions of its RCS sequence are computed. Then, the KL divergence between this distribution and the pre-trained distributions is also computed. The label of this target is determined by these KL divergences and an adaptive threshold. The method collects statistical features and dynamic features of RCS sequences, and provides a unified solution for classifying RCS sequences, which is of great importance for radar target recognition. The efficiency of this method is verified by experiments on simulated data.

Keywords

• radar target recognition
• radar cross-sections
• statistical distributions
• signal classification
• statistical analysis
• pattern classification
• image classification
• feature extraction
• learning (artificial intelligence)
• radar target recognition
• kl divergence
• radar cross section
• kurtosis
• radar target classification
• rcs classification method
• kullback–leibler divergence
• discrete probability distributions
• pre-trained distributions
• ication
• rcs sequences
• statistical features
• dynamic features