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Central DOA Estimation Method for Exponential-Type Coherent Distributed Source Based on Fourth-Order Cumulant

机译:基于四阶累积累积的指数型相干分布源的中央DOA估计方法

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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.
机译:应用于分布式源的稀疏阵列的到达方式(DOA)估计的性能比应用于点源模型的稀疏阵列的估计。在本文中,我们将具有大阵列孔径的CopRime阵列引入指数型相干分布源的DOA估计算法。特别地,我们专注于当信号是非高斯时,可以提供更多有用信息的接收信号的四阶累积量(Foc)。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特性组合来延伸阵列孔,这提高了估计精度和自由度(DOF)。首先,建立稀疏阵列的信号接收模型,并且基于分布式源的特性来计算稀疏阵列的接收信号的四阶累积矩阵,其延伸阵列孔。然后,虚拟阵列由物理阵列元素的SUM聚合构成,并且获得其最大连续部分阵列元件的位置集。最后,通过子空间方法实现了分布式源的中心DOA估计。当阵列元件相同时,所提出的算法的精度和DOF的分布式信号参数估计器(DSPE)算法和最小二乘估计信号参数高于分布式信号参数估计(DSPE)算法和最小二乘估计信号参数。提供复杂性分析和数值模拟以证明所提出的方法的优越性。

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