Adaptive waveforms for target class discrimination

This paper compares the performance of two matched-illumination waveform design techniques for distinguishing between M target hypotheses. The waveforms are implemented within a closed-loop, sequential-testing framework. In contrast to our earlier work, in this paper the target hypotheses are statistically characterized by power spectral densities. Thus, the waveforms are matched to the target class rather than to individual target realizations. As the class probabilities change in response to received data, the waveforms are adapted, which leads to faster decisions.