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基于主奇異矢量的L型陣列相干信號二維DOA估計方法

唐曉杰,  何明浩,  馮明月,  陳昌孝,  韓俊

唐曉杰, 何明浩, 馮明月, 陳昌孝, 韓俊. 基于主奇異矢量的L型陣列相干信號二維DOA估計方法[J]. 電子與信息學報, 2020, 42(11): 2579-2586. doi: 10.11999/JEIT190455
引用本文: 唐曉杰, 何明浩, 馮明月, 陳昌孝, 韓俊. 基于主奇異矢量的L型陣列相干信號二維DOA估計方法[J]. 電子與信息學報, 2020, 42(11): 2579-2586. doi: 10.11999/JEIT190455
Xiaojie TANG, Minghao HE, Mingyue FENG, Changxiao CHEN, Jun HAN. Two-dimensional DOA Estimation Method for L-shaped Array of Coherent Signals Based on Main Singular Vector[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2579-2586. doi: 10.11999/JEIT190455
Citation: Xiaojie TANG, Minghao HE, Mingyue FENG, Changxiao CHEN, Jun HAN. Two-dimensional DOA Estimation Method for L-shaped Array of Coherent Signals Based on Main Singular Vector[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2579-2586. doi: 10.11999/JEIT190455

基于主奇異矢量的L型陣列相干信號二維DOA估計方法

doi: 10.11999/JEIT190455 cstr: 32379.14.JEIT190455

  • 空軍預警學院 武漢 430019

基金項目: 湖北省自然科學基金(2019CFB383)
詳細信息
    作者簡介:

    唐曉杰：男，1995年生，博士生，研究方向為信號與信息處理

    何明浩：男，1963年生，教授，研究方向為信號與信息處理

    馮明月：男，1988年生，講師，研究方向為信號與信息處理

    陳昌孝：男，1982年生，講師，研究方向為信號與信息處理

    韓俊：男，1983年生，講師，研究方向為信號與信息處理

    通訊作者:

    唐曉杰　944717905@qq.com

  • 中圖分類號: TN911.7

Two-dimensional DOA Estimation Method for L-shaped Array of Coherent Signals Based on Main Singular Vector

  • Air Force Early Warning Academy, Wuhan 430019, China

Funds: The Natural Science Foundation of Hubei Province (2019CFB383)
  • 摘要: 針對現(xiàn)有L型陣列相干信號DOA估計算法精度不高、孔徑損失較大的問題，該文提出一種基于主奇異矢量的解相干(L-PUMA)方法以及改進的主奇異矢量法(L-MPUMA)。L-PUMA算法首先對互協(xié)方差矩陣進行降噪，再通過奇異值分解得到2維主奇異矢量，然后利用加權最小二乘法得到線性預測方程的多項式系數(shù)，該線性預測方程的根即為信號的DOA估計，最后提出一種新的配對算法實現(xiàn)仰角和方位角的配對。L-MPUMA算法利用反向共軛變換構造增廣主奇異矢量，進一步提高了數(shù)據(jù)利用率，克服了信號完全相干時L-PUMA算法性能下降嚴重的問題，仿真實驗驗證了所提算法的高效性。
    Abstract: In order to handle the problem that the existing DOA estimation algorithm for L-shaped array of coherent signals is not accurate and the aperture loss is large, a method named L-shaped array Principal-singular-vector Utilization for Modal Analysis (L-PUMA) and its modified algorithm named L-shaped array Modified PUMA (L-MPUMA) are proposed. L-PUMA algorithm first denoises the cross-covariance matrix, then obtains the two-dimensional main singular vector by singular value decomposition, and then obtains the polynomial coefficient of the linear prediction equation by weighted least squares method. The root of the linear prediction equation is the DOA estimation of the signals. Finally, a new pairing algorithm is proposed to realize the pairing of elevation and azimuth. L-MPUMA algorithm uses the inverse conjugate transform to obtain the augmented main singular vector, which further improves the data utilization rate and overcomes the problem that the performance of L-PUMA deteriorates seriously when the signals are completely coherent. Simulation experiments verify the efficiency of the proposed algorithm.
    • HTML全文

  • 圖  1  L型陣列幾何結構

    圖  2  配對算法RMSE隨SNR的變化

    圖  3  配對成功率隨角度間隔的變化

    圖  4  RMSE隨SNR的變化(部分相干)

    圖  5  RMSE隨SNR的變化(完全相干)

    圖  6  RMSE隨快拍數(shù)的變化(部分相干)

    圖  7  RMSE隨快拍數(shù)的變化(完全相干)

    圖  8  目標的空間分布(1組相干信號)

    圖  9  目標的空間分布(2組相干信號)

    圖  10  目標的空間分布(3組相干信號)

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出版歷程
  • 收稿日期:  2019-06-20
  • 修回日期:  2020-04-01
  • 網(wǎng)絡出版日期:  2020-08-29
  • 刊出日期:  2020-11-16

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