Waveform scheduling via directed information in cognitive radar

The objective of waveform scheduling is to achieve maximal information extraction of the radar scene, which typically changes from one measurement to the next, by exploiting prior statistics and waveform diversity. In this paper waveform scheduling is addressed using information theoretic concepts. A pre-defined waveform library is assumed to be given, or designed a priori. To keep the analysis simple, we constrain ourselves to a library comprised of two waveforms scheduled over two consecutive time intervals. We propose selecting the waveforms to maximize the directed information, a metric not previously considered in this context, which directly incorporates the feedback present in the radar system. Analog and discrete models are discussed; the former allows for a spectral domain interpretation, whereas, the latter permits analogies to Bayesian error metrics.