Object detection based deinterleaving of radar signals using deep learning for cognitive EW

Abstract

AbstractIn a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.