This paper develops an odometry algorithm for unmanned air vehicles (UAVs) based solely on radar measurements. The radar return from stationary ground-based scatterers is used to create a range-compressed image, which can be used to estimate the relative motion of the UAV. The Hough transform is used to extract scatterer features from the range-compressed image and to solve the data association problem. The estimated relative motion of the aircraft based on radar return is then fused with inertial measurement unit data in an extended Kalman filter. Radar data collected during actual flight tests are compared to truth data as estimated using a global positioning system sensor to demonstrate the effectiveness of the algorithm. Robustness properties of the algorithm are explored using simulation studies.


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    Title :

    Radar odometry on fixed-wing small unmanned aircraft


    Contributors:


    Publication date :

    2016-02-01


    Size :

    1785588 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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