A Radar Signal Deinterleaving Method Based on Mixture Distribution Test With a Sequential Two-Module Neural Network Architecture

Radar signal deinterleaving (RSD) through pulse description word (PDW) clustering is a crucial prestep for electronic reconnaissance. Although clustering algorithms are usually universal, existing PDW clustering algorithms cannot achieve a satisfying performance in a dense and complex electromagnetic environment. This article presents a hierarchical clustering method (MDT-HC) based on the mixture distribution test (MDT), which combines learning the simulated PDW’s distribution as prior. The algorithm starts by regressing the marginal distribution of PDW attributes to determine the cluster number and the sub-distribution’s parameter information, such as the mean value, semi-range, and relative proportion. Then, the data are divided into overlapping and nonoverlapping areas referenced by these parameters, and the points in the overlapping area are iteratively classified to both sides using k-nearest neighbor (KNN) with nonoverlapping points’ pure statistical features serving as a reference. The clustered result can be further sent into a trained sequential network for radiation source identification. The proposed algorithm’s time complexity and purity’s upper bound are analyzed theoretically from the perspective of geometric distribution properties. Its effectiveness and robustness have been validated through the measured data in the experiments. Besides, it has achieved the top 1% in the big data challenge competition.