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Raw Data Reduction for Synthetic Aperture Based on SVD-QR

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Book cover Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 386))

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Abstract

Singular-Value-QR Decomposition (SVD-QR) is a new data reduction algorithm, which can be applied to compress high data redundancy in wireless communication system and radar system. It selects the most important part of the original data which can represent other information as well. Based on the analysis of synthetic aperture radar (SAR) history data in slow-time domain, we prove that it satisfies the condition of SVD-QR approach. In addition, backprojection image reconstruction algorithm is also presented in this work, which is more efficient than matched filter method. Simulations are performed according to the Gotcha Volumetric SAR Data Set, which is collected in a real 2D/3D scenario. From the simulation results, the effectiveness of this new algorithm is verified and the compression ratio can be achieved by 2:5. Comparing with the uniform down sampling method, SVD-QR algorithm can save about 60 % data and have a better performance.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Na Wu & Qilian Liang

Authors
  1. Na Wu
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  2. Qilian Liang
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Corresponding author

Correspondence to Na Wu .

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Editors and Affiliations

  1. University of Texas at Arlington, ARLINGTON, Texas, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Jiasong Mu

  3. Tianjin Normal University, Tianjin, China

    Wei Wang

  4. College of Physical and Electronic Infor, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Wu, N., Liang, Q. (2016). Raw Data Reduction for Synthetic Aperture Based on SVD-QR. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49831-6_19

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  • DOI: https://doi.org/10.1007/978-3-662-49831-6_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49829-3

  • Online ISBN: 978-3-662-49831-6

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