Tracking the Operational Mode of Multi-Function Radar

Abstract

<p> This thesis presents a novel hybrid methodology using Recurrent Neural Network and Dynamic Time Warping to solve the mode estimation problem of a radar warning receiver (RWR). The RWR is an electronic support (ES) system with the primary objective to estimate the threat posed by an unfriendly (hostile) radar in an electronic warfare (EW) environment. One such radar is the multi-function radar (MFR), which employs complex signal architecture to perform multiple tasks. As the threat posed by the radar directly depends on its current mode of operation, it is vital to estimate and track the mode of the radar. The proposed method uses a recurrent neural network (echo state network and recurrent multi-layer perceptron) trained in a supervised manner, with the dynamic time warping algorithm as the post processor to estimate the mode of operation. A grid filter in Bayesian framework is then applied to the dynamic time warp estimate to provide an accurate posterior estimate of the operational mode of the MFR. This novel approach is tested on an EW scenario via simulation by employing a hypothetical MFR. Based on the simulation results, we conclude that the hybrid echo state network is more suitable than its recurrent multi-layer perceptron counterpart for the mode estimation problem of a RWR.</p>