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[Keyword] metric reconstruction(2hit)

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  • Ground Moving Target Indication for HRWS-SAR Systems via Symmetric Reconstruction

    Hongchao ZHENG  Junfeng WANG  Xingzhao LIU  Wentao LV  

     
    PAPER-Digital Signal Processing

      Vol:
    E99-A No:8
      Page(s):
    1576-1583

    In this paper, a new scheme is presented for ground moving target indication for multichannel high-resolution wide-swath (HRWS) SAR systems with modified reconstruction filters. The conventional steering vector is generalized for moving targets through taking into account the additional Doppler centroid shift caused by the across-track velocity. Two modified steering vectors with symmetric velocity information are utilized to produce two images for the same scene. Due to the unmatched steering vectors, the stationary backgrounds are defocused but they still hold the same intensities in both images but moving targets are blurred to different extents. The ambiguous components of the moving targets can also be suppressed due to the beamforming in the reconstruction procedure. Therefore, ground moving target indication can be carried out via intensity comparison between the two images. The effectiveness of the proposed method is verified by both simulated and real airborne SAR data.

  • A Linear Metric Reconstruction by Complex Eigen-Decomposition

    Yongduek SEO  Ki-Sang HONG  

     
    PAPER

      Vol:
    E84-D No:12
      Page(s):
    1626-1632

    This paper proposes a linear algorithm for metric reconstruction from projective reconstruction. Metric reconstruction problem is equivalent to estimating the projective transformation matrix that converts projective reconstruction to Euclidean reconstruction. We build a quadratic form from dual absolute conic projection equation with respect to the elements of the transformation matrix. The matrix of quadratic form of rank 2 is then eigen-decomposed to produce a linear estimate. The algorithm is applied to three different sets of real data and the results show a feasibility of the algorithm. Additionally, our comparison of results of the linear algorithm to results of bundle adjustment, applied to sets of synthetic image data having Gaussian image noise, shows reasonable error ranges.