Proceedings PaperDetection of linear objects for synthetic vision applications
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This paper describes an approach to find linear objects, e.g. powerlines and runways for synthetic vision applications with a multifunction 35 GHz radar. The approach is based on a combination of traditional radar signal processing like the CFAR-algorithm and image processing techniques like the Hough transform. It is assumed that the objects are visible as a sequence of single reflectors on a line. The proposed method ensures that the probability of detection or the false alarm rate of a linetype object is independent of the position. In the first step, a CFAR-algorithm detects the possible points along the line. All detected objects are describes by a list of attributes, from which some relevant ones can be chosen. Subsequently they are transformed to the Hough space, where lines are described by a slope and a distance parameter. A threshold is calculated which ensures a constant false alarm rate or a constant probability of detection. In the next step a cluster algorithm with a special distance measure is used to find all possible lines in the Hough-space. After transforming back to the original space, the plausibility is checked and a final selection is done. The performance of the approach is shown by applying the method described above to simulated and measured data. The paper describes the calculation of the false alarm rate, the probability of detection and the calculation of the threshold.