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Cognitive chaotic UWB-MIMO detect-avoid radar for autonomous UAV navigation

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Nijsure, Yogesh Anil et Kaddoum, Georges et Khaddaj Mallat, Nazih et Gagnon, Ghyslain et Gagnon, François. 2016. « Cognitive chaotic UWB-MIMO detect-avoid radar for autonomous UAV navigation ». IEEE Transactions on Intelligent Transportation Systems, vol. 17, nº 11. p. 3121-3131.
Compte des citations dans Scopus : 3.

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Résumé

A cognitive detect and avoid radar system based on chaotic UWB-MIMO waveform design to enable autonomous UAV navigation is presented. A Dirichlet-Process-Mixture-Model (DPMM) based Bayesian clustering approach to discriminate extended targets and a Change-Point (CP) detection algorithm are applied for the autonomous tracking and identification of potential collision threats. A DPMM based clustering mechanism does not rely upon any a priori target scene assumptions and facilitates online multivariate data clustering/classification for an arbitrary number of targets. Furthermore, this radar system utilizes a cognitive mechanism to select efficient c haotic waveforms to facilitate enhanced target detection and discrimination. We formulate the CP mechanism for the online tracking of target trajectories which present a collision threat to the UAV navigation and thus we supplement the conventional Kalman filter based tracking. Simulation results demonstrate a significant performance improvement for the DPMM-CP assisted detection as compared with direct generalized likelihood ratio based detection. Specifically, we o bserve a 4 dB performance g ain in target detection over conventional fixed UWB waveforms a nd superior collision avoidance capability offered by the joint DPMM-CP mechanism.

Type de document: Article publié dans une revue, révisé par les pairs
Professeur:
Professeur
Kaddoum, Georges
Gagnon, Ghyslain
Gagnon, François
Affiliation: Génie électrique, Génie électrique, Génie électrique
Date de dépôt: 30 mai 2016 16:10
Dernière modification: 11 sept. 2018 17:12
URI: http://espace2.etsmtl.ca/id/eprint/12660

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