Paper Info
Title
Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation.
Abstract
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 chaotic 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; 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 observe a 4-dB performance gain in target detection over conventional fixed UWB waveforms and superior collision avoidance capability offered by the joint DPMM-CP mechanism.
Year
DOI
Venue
2016
10.1109/TITS.2016.2539002
IEEE Trans. Intelligent Transportation Systems
Keywords
Field
DocType
Radar detection,Chaos,Navigation,Collision avoidance,Clustering algorithms,Bayes methods
Radar,Computer vision,Detect and avoid,Simulation,A priori and a posteriori,MIMO,Collision,Artificial intelligence,Engineering,Cluster analysis,Chaotic,Performance improvement
Journal
Volume
Issue
ISSN
17
11
1524-9050
Citations 
PageRank 
References 
0
0.34
16
Authors
5
Name
Order
Citations
PageRank
Yogesh Nijsure11186.74
Georges Kaddoum287494.42
Nazih Khaddaj Mallat3325.76
Ghyslain Gagnon46711.40
François Gagnon523313.25