Abstract
This work presents a cognitive waveform selection mechanism for chaotic ultra-wideband multiple-input multiple-output (MIMO) radars. It utilizes the target discrimination capability of a Dirichlet process mixture model (DPMM)-based clustering approach to discriminate individual extended targets and applies a mutual information (MI)-based mechanism to select the best transmission waveform. This joint DPMM-MI cognitive mechanism aims at enhancing target discrimination and detection, showing a 3-dB performance gain in achieving 0.9 target detection probability over conventional MIMO radar waveforms.
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