ABSTRACT In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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互质线性阵列的计算高效 DOA 估计：一种连续信号子空间拟合算法

摘要 本文研究了互质阵列对多信号的到达方向（DOA）估计，并将信号子空间拟合（SSF）方法与互质阵列联系起来，获得了比传统均匀阵列更好的DOA估计性能。虽然由于多维全局角度搜索，SSF 方法在多个信号的情况下需要昂贵的计算成本，但我们从计算效率的角度提出了连续 SSF (S-SSF) 算法。在所提出的算法中，我们采用旋转不变性和互质属性来获得初始估计。然后，通过一个连续的方案，我们将传统的多维全局角度搜索问题转化为一维局部角度搜索问题。最后，计算复杂度显着降低。具体而言，所提出的 S-SSF 算法可以获得与 SSF 几乎相同的 DOA 估计性能，但复杂度显着降低。最后，提供了 Cramer-Rao Bound (CRB)，数值模拟证明了所提出算法的有效性。