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基于酉变换的求根稀疏渐近最小方差的离格方位估计方法

A method of root-sparse asymptotic minimum variance off-grid DOA estimation based on unitary transformation

  • 摘要: 为提高方位估计精度,提升运算效率,文章提出一种改进的求根稀疏渐近最小方差离格方位估计方法。该方法利用酉变换,将复数域的协方差矩阵信号转化到实数域,减少了迭代的运算量,降低了运行时间,且酉变换具有双向平滑的作用,提高了相干信源的估计性能和角度分辨能力;此外,结合离网格模型,将网格点的位置作为估计参数直接进行迭代运算,克服了离网格间隙引起的建模误差,提高了方位估计的精度。计算仿真和湖上试验数据处理结果表明,文中所提方法在低信噪比和少快拍情况下的方位估计性能优势更为明显,在实际场景中具有较好的适用性,具有较大的工程应用价值。

     

    Abstract: To enhance the accuracy and efficiency of direction of arrival (DOA) estimation, an improved method of root-sparse asymptotic minimum variance off-grid DOA estimation is proposed in this paper. By utilizing unitary transformation, the signal of covariance matrix in complex domain is transformed into real domain, which reduces computation and runtime of iteration. Moreover, the unitary transformation has the function of bidirectional smoothing, which improves estimation performance and angle resolution of coherent sources. Additionally, combined with the off-grid model, grid points' positions are directly used as estimation parameters for iterative calculation. It overcomes modeling errors caused by off-grid gaps and improves azimuth estimation accuracy, particularly in scenarios with small snapshots and low signal to noise ratio (SNR) . Simulation results and lake experiment data demonstrate the effectiveness of the proposed method, which will be highly valuable for engineering application.

     

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