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.


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    Title :

    Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation




    Publication date :

    2016-11-01


    Size :

    1789922 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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