Radar pulse train parameter estimation and tracking using neural networks

Abstract

The post-deinterleaving radar pulse train problem requires estimation of the parameters and tracking of the individual pulse trains. A simple recurrent backpropagation neural network is used based on a simple state space time series formulation of the radar problem. The network incorporates a novel heuristic adaptive error threshold that allows simultaneously good tracking and parameter estimating abilities. Two simple but revealing examples are presented to show how the network is robust to missing and spurious pulses, as well as multiple level staggers with discontinuous mode changes.