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.
Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian technique
IEEE Transactions on Aerospace and Electronic Systems ; 51 , 3 ; 2360-2378
2015-07-01
3883129 byte
Article (Journal)
Electronic Resource
English
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