Automatic antenna scan type classification for next‐generation electronic warfare receivers

Abstract

Cognitive electronic warfare (EW) is the key feature for next-generation EW systems. The deinterleaving and classification of received radar pulses and selection of the proper electronic attack technique are the common issues for non-preprogrammed cognitive jamming systems. Antenna scan type (AST) is a dominant parameter to discriminate and solve the ambiguities for the classification of the radar threats. In the literature, there are solutions for the classification of conical, helical, raster, and bidirectional scan types. In this study, spiral and electronic scan types are introduced in the AST analysis algorithm. Moreover, a new method based on correlation is proposed to determine the main beam signal. The proposed method gives better performance than amplitude thresholding-based algorithms for not only newly added but also other scan types. Also, main beam flatness ratio is proposed as a new feature to separate electronic scan types from the other scan types. Intensive simulations are executed to evaluate the performance of the proposed new method and feature. The classification of the conical, spiral, helical, circular, raster, and sector scan types at ~15 dB signal-to-noise ratio (SNR) is completed with 98% success. For electronic scan types, at least 94% classification accuracy is observed for SNR values above 20 dB.