一种高精度低复杂度的改进 Root －MUSIC 算法.

An Improved Root-MUSIC Algorithm with High Precision and Low Complexity.

Aiming at the precision loss problem of most low-complexity Root-MUSIC algorithms at present, a low-complexity Root-MUSIC algorithm with precision compensation ability is studied and proposed. The algorithm reconstructs the autocorrelation matrix with Toeplitz shape according to the first row of the approximate data observation matrix obtained by finite snapshots, so that the reconstructed autocorrelation matrix has Hermitian property. After decomposing the reconstructed autocorrelation matrix, the noise subspace is obtained, the noise subspace is flipped and split, a new root-finding polynomial is reconstructed, and then the DOA estimated value is obtained by the root-finding method. The algorithm proposed in this paper using Toeplitz matrix reconstruction and root polynomial reduction effectively improves the DOA estimation accuracy of the improved Root-MUSIC algorithm. And the time complexity of the improved algorithm is no higher than that of previous algorithms. Under different incident sources and sampling snapshots, the algorithm proposed in this paper also shows stronger robustness and stability. [ABSTRACT FROM AUTHOR]

针对目前多数低复杂度 Root －MUSIC 算法的精度损失问题ꎬ研究并提出了一种具备精度补偿 能力的低复杂度 Root －MUSIC 算法.该算法依据有限快拍数得到的近似数据观测矩阵首行重构具有 Toeplitz 形态的自相关矩阵ꎬ使重构的自相关矩阵具备 Hermitian 性ꎻ对重构的自相关矩阵特征值分解后获得噪声子空 间ꎬ并将噪声子空间翻转拆分ꎬ重构新的求根多项式ꎬ进而通过求根方法得到 DOA 估计值. 本文算法通过 Toeplitz 矩阵重构及求根多项式降阶ꎬ不但有效提高了改进 Root －MUSIC 算法的 DOA 估计精度ꎬ同时改进 算法的时间复杂度不高于前人算法ꎻ在不同的入射信源及采样快拍数下ꎬ本文算法表现出更强的鲁棒性和稳 定性. [ABSTRACT FROM AUTHOR]

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