The performance of direction-of-arrival (DOA) estimation for sparse arrays applied to the distributed source is worse than that applied to the point source model. In this paper, we introduce the coprime array with a large array aperture into the DOA estimation algorithm of the exponential-type coherent distributed source. In particular, we focus on the fourth-order cumulant (FOC) of the received signal which can provide more useful information when the signal is non-Gaussian than when it is Gaussian. The proposed algorithm extends the array aperture by combining the sparsity of array space domain with the fourth-order cumulant characteristics of signals, which improves the estimation accuracy and degree of freedom (DOF). Firstly, the signal-received model of the sparse array is established, and the fourth-order cumulant matrix of the received signal of the sparse array is calculated based on the characteristics of distributed sources, which extend the array aperture. Then, the virtual array is constructed by the sum aggregate of physical array elements, and the position set of its maximum continuous part array element is obtained. Finally, the center DOA estimation of the distributed source is realized by the subspace method. The accuracy and DOF of the proposed algorithm are higher than those of the distributed signal parameter estimator (DSPE) algorithm and least-squares estimation signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm when the array elements are the same. Complexity analysis and numerical simulations are provided to demonstrate the superiority of the proposed method.

中文翻译：

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基于四阶累积量的指数型相干分布源的中心DOA估计方法

应用于分布式源的稀疏数组的到达方向（DOA）估计性能比应用于点源模型的性能差。在本文中，我们将具有大阵列孔径的互质阵列引入到指数型相干分布源的DOA估计算法中。特别地，我们集中于接收信号的四阶累积量（FOC），当信号为非高斯信号时，它可以提供比其为高斯信号时更多的有用信息。所提出的算法通过将阵列空间域的稀疏性与信号的四阶累积量特征相结合来扩展阵列孔径，从而提高了估计精度和自由度。首先，建立稀疏阵列的信号接收模型，并根据分布源的特性计算出稀疏阵列接收信号的四阶累积量矩阵，扩展了阵列孔径。然后，通过物理数组元素的总和构造虚拟数组，并获得其最大连续部分数组元素的位置集。最后，通过子空间方法实现了分布式源的中心DOA估计。当阵列元素相同时，所提算法的精度和自由度要高于分布式信号参数估计器（DSPE）算法和最小二乘估计算法（LS-ESPRIT）算法。提供了复杂度分析和数值模拟，以证明该方法的优越性。