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Accepted Papers

  • CFAR Detection in MIMO Radars using Fuzzy Fusion Rules in Homogeneous Background
    Faycal Khaldi , Faouzi Soltani , Departementd electronique, Universitè des Frères Mentouri Constantine Constantine 25000, Algeria
    ABSTRACT

    This paper aims to put forward a seamless mosaic method of UAV image for dense urban area, which can effectively avoid seam-line pass through the edge of the building, so as to eliminate the ghosting, dislocation and seam in the image mosaic process. Firstly, the radiation error of UAV image are corrected by Wallis algorithm, and extract the corresponding points from the adjacent images by SIFT algorithm, to correct the left and right pending matching images to the virtual unified reference image, to ensure the images are in the same coordinate system. Then, in view of the shortcomings of the classical Duplaquet method, we proposed a new more robust UAV image mosaic algorithm by changing the energy accumulation criterion for energy function of dynamic programming. Finally, the comparative experiments show that our method can find the optimal seam-line to avoid it through the edge of houses, especially in dense urban area..

  • RADAR TARGET CLASSIFICATION WITH RBF NEURAL NETWORKS
    Department of Electrical & Computer Engineering, Ben-Gurion University of the Negev,Beer-Sheva, Israel
    ABSTRACT

    This paper introduces a new method for classification of ground moving targets detected by Ground Moving Target Indication (GMTI) radar systems,based on artificial neural networks. The direct information provided by GMTI radars does not include any information regarding the type of vehicles which are detected. On the other hand, the ability of using GMTI radar measurements to classify ground moving targets, even roughly, is of great interest. The approach suggested is based on Radial Basis Function (RBF) neural network. The data used as features for classification is composed of Radar Cross Section (RCS)values of thetarget obtained in varying aspect angles. The proposed classifier was tested on diverse simulative cases and yielded over 90% correct estimation in classification for three groups of size, and over 85% correct estimation in classification for five groups


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