Title :
Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian technique
Author :
Nijsure, Yogesh ; Kaddoum, Georges ; Leung, Henry
Author_Institution :
Ecole de Technol. Super. Montreal, Montreal, QC, Canada
fDate :
7/1/2015 12:00:00 AM
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.
Keywords :
Bayes methods; MIMO radar; object detection; pattern clustering; probability; radar detection; ultra wideband radar; DPMM-MI cognitive mechanism; Dirichlet process mixture model; clustering approach; cognitive chaotic UWB-MIMO radar; cognitive waveform selection mechanism; gain 3 dB; mutual information; nonparametric Bayesian technique; target detection probability; target discrimination; transmission waveform; ultrawideband multiple-input multiple-output radar; Bayes methods; MIMO; MIMO radar; Receiving antennas; Scattering;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2015.140373
