Nijsure, Yogesh Anil, Kaddoum, Georges, Khaddaj Mallat, Nazih, 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.
pp. 3121-3131.
Compte des citations dans Scopus : 22.
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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: | https://espace2.etsmtl.ca/id/eprint/12660 |
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