Cognitive Radar Tracking Performance Enhancement via Waveform Optimization

Abstract

This paper addresses target tracking using cognitive radars and discusses the effect of radar waveform parameters optimization on the tracking performance. Flying targets are not typically governed by one uniform motion model but rather can go through a diversity of arbitrary manoeuvres. Facing the change in the target dynamics, it is necessary to design the radar parameters adaptively to guarantee a better performance. To counterbalance the target motion model uncertainty, we are proposing an IMM-based algorithm for target tracking and waveform optimization to allow the cognitive radar to adapt to the continuous changes of the target dynamics. In this approach the target state vector estimate resulting from the IMM algorithm will be taken to adaptively change the radar parameters such as the radar pulse repetition frequency (PRF), the number of integrated pulses N, the radar scan schedule and the detection threshold. Simulation results will show that optimizing the radar waveform parameters enhances the radar tracking performance.