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基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计

李海 刘新龙 蒋婷 吴仁彪

李海, 刘新龙, 蒋婷, 吴仁彪. 基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计[J]. 电子与信息学报, 2016, 38(12): 3224-3229. doi: 10.11999/JEIT160132
引用本文: 李海, 刘新龙, 蒋婷, 吴仁彪. 基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计[J]. 电子与信息学报, 2016, 38(12): 3224-3229. doi: 10.11999/JEIT160132
LI Hai, LIU Xinlong, JIANG Ting, WU Renbiao. Estimation of Clutter Degrees of Freedom in Airborne Forward-looking Radar via Random Matrix Theory and Minimum Description Length Criteria[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3224-3229. doi: 10.11999/JEIT160132
Citation: LI Hai, LIU Xinlong, JIANG Ting, WU Renbiao. Estimation of Clutter Degrees of Freedom in Airborne Forward-looking Radar via Random Matrix Theory and Minimum Description Length Criteria[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3224-3229. doi: 10.11999/JEIT160132

基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计

doi: 10.11999/JEIT160132
基金项目: 

国家自然科学基金(61471365, 61571442, 61231017)

中央高校基本科研业务费项目(3122015B002),中国民航大学蓝天青年学者培养经费

Estimation of Clutter Degrees of Freedom in Airborne Forward-looking Radar via Random Matrix Theory and Minimum Description Length Criteria

Funds: 

The National Natural Science Foundation of China (61471365, 61571442, 61231017), The National Universitys Basic Research Foundation of China (3122015B002), The Foundation for Sky Young Scholars of Civil Aviation University of China

  • 摘要: 有限训练样本时,总体协方差矩阵特征谱的严重扩展使得机载前视阵雷达杂波自由度估计困难。该文提出一种前视阵杂波自由度估计方法,该方法利用随机矩阵理论(Random Matrix Theory, RMT)中特征值统计分布特性建立参数化的概率模型,结合最小描述长度(Minimum Description Length, MDL)准则关于信源检测的思想估计杂波自由度。该方法能够在有限训练样下实现杂波自由度的有效估计,仿真结果验证了方法的有效性。
  • GUERCI J R. Space-Time Adaptive Processing for Radar [M]. Norwood, Artech House, 2014: 1-74.
    FERTIG L B. Analytical expressions for Space-Time Adaptive Processing (STAP) performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 42-53. doi: 10.1109/TAES.2014.130676.
    DEGURSE J F, SAVY L, and MARCOS S. Reduced-rank STAP for target detection in heterogeneous environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1153-1162. doi: 10.1109/TAES.2014.120414.
    BRENNAN L E and STAUDAHER F M. Subclutter visibility demonstration[R]. Technical Report RL-TR-92-21, Adaptive Sensors Incorporated, 1992.
    同压龙, 王彤, 文才, 等. 一种稳健的机载非正侧视阵雷达杂波抑制方法[J]. 电子与信息学报, 2015, 37(5): 1044-1050. doi: 10.11999/JEIT141222.
    TONG Yalong, WANG Tong, WEN Cai, et al. A robust clutter suppression method for airborne non-sidelooking radar[J]. Journal of Electronics Information Technology, 2015, 37(5): 1044-1050. doi: 10.11999/JEIT141222.
    BAI Z D and SILVERSTEIN J W. Spectral Analysis of Large Dimensional Random matrices[M]. New York: Springer, 2010: 1-13.
    ZWINGELSTEIN C M and DEBBAH M. Random matrix theory based resource allocation in correlated MIMO systems with ARQ feedback[J]. IEEE Communications Letters, 2014, 18(5): 793-796. doi: 10.1109/LCOMM.2014.031414.140214.
    KRITCHMAN S and NADLER B. Non-parametric detection of the number of signals: hypothesis testing and random matrix theory[J]. IEEE Transactions on Signal Processing, 2009, 57(10): 3930-3941. doi: 10.1109/TSP.2009.2022897.
    VALLET P, LOUBATON P, and MESTRE X. Improved subspace estimation for multivariate observations of high dimension: the deterministic signals case[J]. IEEE Transactions on Information Theory, 2012, 58(2): 1043-1068.
    AHMED A, HU Y F, NORAS J M, et al. Random matrix theory based spectrum sensing for cognitive radio networks [C] Internet Technologies and Applications, Wrexham, 2015: 479-483.
    WAX M and KAILATH T. Detection of signals by information theoretic criteria[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 387-392. doi: 10.1109/TASSP.1985.1164557.
    BARRON A, RISSANEN J, and YU B. The minimum description length principle in coding and modeling[J]. IEEE Transactions on Information Theory, 1998, 44(6): 2743-2760. doi: 10.1109/18.720554.
    HUANG L and SO H C. Source enumeration via MDL criterion based on linear shrinkage estimation of noise subspace covariance matrix[J]. IEEE Transactions on Signal Processing, 2013, 61(19): 4806-4821. doi: 10.1109/TSP. 2013.2273198.
    HAIMOVICH A M and AYOUB T F. The eigencanceler: Space time adaptive radar by eigenanalysis methods[R]. New Jersey Inst of Tech Newark, 1999.
    MARCENKO V A and PASTUR L A. Distribution of eigenvalues for some sets of random matrices[J]. Sbornik: Mathematics, 1967, 1(4): 457-483.
    NADAKUDITI R R and EDELMAN A. Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples[J]. IEEE Transactions on Signal Processing, 2008, 56(7): 2625-2638. doi: 10. 1109/TSP.2008. 917356.
    BAI Z D and SILVERSTEIN J W. CLT for linear spectral statistics of large-dimensional sample covariance matrices[J]. Annals of Probability, 2004, 32(1): 553-605.
    WARD J. Space-time adaptive processing for airborne radar [R]. Technical Report 1015, MIT Lincoln Laboratory, 1994.
    REED I S, MALLETT J D, and BRENNAN L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(4): 853-863. doi: 10.1109/TAES.1974.307893.
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出版历程
  • 收稿日期:  2016-01-29
  • 修回日期:  2016-06-23
  • 刊出日期:  2016-12-19

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