Adaptive waveform design for radar target classification

Abstract

The typical approach to radar target classification is to image the target with waveforms that provide high resolution and low sidelobes, and then to compare the target images to a template library. In this chapter, we reconsider whether imaging-based metrics for waveform design are best for target classification, and develop alternative design strategies that result in waveforms with improved classification ability, but not necessarily a good ambiguity function by traditional notions. After presenting waveform design strategies based on optimizing signal-to-noise ratio or mutual information from a wide-sense stationary (WSS) ensemble of target impulse responses, we apply the design methods to the problem of radar target classification through a two-step process. The first step is to modify the design methodology that was based on WSS targets to account for the finite duration of practical target responses. The second step is to use the target class probabilities and impulse response library to calculate a weighted power spectral variance over target classes, which is then substituted into the design equations. The use of target class probabilities enables the waveform to be adapted in response to previous transmissions. Waveform behaviour and performance are studied over several different clutter and noise scenarios. The target impulse response library for these studies is based on finite-difference time-domain (FDTD) simulation of a publically available CAD model of an F-16 aircraft.