ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Reconstruction characteristic and station layout optimization of distributed radar sparse imaging

  • Key Laboratory of Electromagnetic Space Information of CAS, USTC, Hefei 230027,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.04.007
  • Received Date: 14 November 2013
  • Accepted Date: 27 February 2014
  • Rev Recd Date: 27 February 2014
  • Publish Date: 30 April 2014
    Abstract

  • Abstract

    The relationship between target reconstruction and station layout in distributed radar sparse imaging system was studied. Based on the sparse imaging model of the distributed radar, the relationship between mutual coherence of measurement matrix and system parameters was derived. Furthermore, it was presented that the mutual coherence was decided only by unit station direction vector under the given conditions of radar parameters and imaging scene partition. Therefore, the cost function “minimization of mutual coherence” was utilized for station layout optimization. Simulation results are provided to demonstrate the performance improvement of the proposed method.

    Abstract

    The relationship between target reconstruction and station layout in distributed radar sparse imaging system was studied. Based on the sparse imaging model of the distributed radar, the relationship between mutual coherence of measurement matrix and system parameters was derived. Furthermore, it was presented that the mutual coherence was decided only by unit station direction vector under the given conditions of radar parameters and imaging scene partition. Therefore, the cost function “minimization of mutual coherence” was utilized for station layout optimization. Simulation results are provided to demonstrate the performance improvement of the proposed method.
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    • References(12)
  • [1]
    Palmer J, Homer J, Longstaff I D, et al. ISAR imaging using an emulated multistatic radar system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1 464-1 472.
    [2]
    Pastina D, Sedehi M, Cristallini D. Passive bistatic ISAR based on geostationary satellites for coastal surveillance[C]// International IEEE Radar Conference. Washington, USA: IEEE Press, 2010: 865-870.
    [3]
    Cristofani E, Kubica V, Neyt X. A multibeam opportunistic SAR system[C]// International Radar Conference. Kansas, USA: IEEE Press, 2011: 778-783.
    [4]
    Antoniou M, Zeng Z, Feifeng L, et al. Experimental demonstration of passive BSAR imaging using navigation satellites and a fixed receiver[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(3): 477-481.
    [5]
    Odendaal J W, Barnard E, Pistorius C W I. Two-dimensional superresolution radar imaging using the MUSIC algorithm[J]. IEEE Transactions on Antennas and Propagation, 1994, 42(10): 1 386-1 391.
    [6]
    Liu C C, Chen W D. Sparse frequency diverse MIMO radar imaging[C]// 46th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE Press, 2012: 853-857.
    [7]
    徐浩, 尹治平, 刘畅畅, 等. 基于压缩感知的稀疏无源雷达成像[J]. 系统工程与电子技术, 2011, 33(12): 2 623-2 630.
    [8]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [9]
    Baraniuk R, Steeghs P. Compressive radar imaging[C]// International Radar Conference. Boston, USA: IEEE Press, 2007: 128-133.
    [10]
    Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006, 52(1): 6-18.
    [11]
    Kirkpatrick S, Jr Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598): 671-680.
    [12]
    Candès E J, Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
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    [1]
    Palmer J, Homer J, Longstaff I D, et al. ISAR imaging using an emulated multistatic radar system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1 464-1 472.
    [2]
    Pastina D, Sedehi M, Cristallini D. Passive bistatic ISAR based on geostationary satellites for coastal surveillance[C]// International IEEE Radar Conference. Washington, USA: IEEE Press, 2010: 865-870.
    [3]
    Cristofani E, Kubica V, Neyt X. A multibeam opportunistic SAR system[C]// International Radar Conference. Kansas, USA: IEEE Press, 2011: 778-783.
    [4]
    Antoniou M, Zeng Z, Feifeng L, et al. Experimental demonstration of passive BSAR imaging using navigation satellites and a fixed receiver[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(3): 477-481.
    [5]
    Odendaal J W, Barnard E, Pistorius C W I. Two-dimensional superresolution radar imaging using the MUSIC algorithm[J]. IEEE Transactions on Antennas and Propagation, 1994, 42(10): 1 386-1 391.
    [6]
    Liu C C, Chen W D. Sparse frequency diverse MIMO radar imaging[C]// 46th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE Press, 2012: 853-857.
    [7]
    徐浩, 尹治平, 刘畅畅, 等. 基于压缩感知的稀疏无源雷达成像[J]. 系统工程与电子技术, 2011, 33(12): 2 623-2 630.
    [8]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [9]
    Baraniuk R, Steeghs P. Compressive radar imaging[C]// International Radar Conference. Boston, USA: IEEE Press, 2007: 128-133.
    [10]
    Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006, 52(1): 6-18.
    [11]
    Kirkpatrick S, Jr Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598): 671-680.
    [12]
    Candès E J, Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
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