Two-dimensional DOA Estimation Method for L-shaped Array of Coherent Signals Based on Main Singular Vector
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摘要: 针对现有L型阵列相干信号DOA估计算法精度不高、孔径损失较大的问题，该文提出一种基于主奇异矢量的解相干(L-PUMA)方法以及改进的主奇异矢量法(L-MPUMA)。L-PUMA算法首先对互协方差矩阵进行降噪，再通过奇异值分解得到2维主奇异矢量，然后利用加权最小二乘法得到线性预测方程的多项式系数，该线性预测方程的根即为信号的DOA估计，最后提出一种新的配对算法实现仰角和方位角的配对。L-MPUMA算法利用反向共轭变换构造增广主奇异矢量，进一步提高了数据利用率，克服了信号完全相干时L-PUMA算法性能下降严重的问题，仿真实验验证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.
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