A Real-Time Radar Signal Sorting Method Under Bayesian Framework With Dynamic Cluster Merging

Traditional radar signal sorting (RSS) methods assume that all types of radar signals can be stored and trained offline. However, the presence of noncooperative radar emitters makes the assumption unrealistic in real-world RSS problems. This article proposes a real-time method for RSS in complex electromagnetic environments based on the Bayesian framework. Each type of radar signal is regarded as a collection of samples from Gaussian distribution. Bayesian inference is applied to calculate the likelihood of a new data point belonging to a known category, and the Chinese restaurant process (CRP) is adopted to provide some prior knowledge which helps calculate the probability of opening a new cluster. For the overclustering problem caused by dynamic and large-scale changes in pulse descriptor words (PDWs) of the signals from the same radar emitter, a dynamic cluster merging (DCM) algorithm is introduced. The Wasserstein distance and Hausdorff distance of the probability density functions (pdfs) of the PDWs between clusters are weighted, and the pulse repetition interval (PRI) is also considered to measure the similarity between clusters which helps merge clusters of the same type of signal. The method proposed in this article is verified under the conditions of large-scale changes in PDWs and dynamic changes in signal numbers over time. Higher accuracy of real-time signal sorting is achieved on the simulated dataset of signals from 30 types of radar emitters compared with existing methods. Even in the situations with a significant pulse loss rate, the proposed algorithm still attains high accuracy.