Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.
Sparse linear array is prone to element failure affected by environmental and other factors in practical application, resulting in the decline of degrees of freedom and the attenuation or even failure of direction of arrival (DOA) estimation performance. To address this problem, a ternary redundant sparse array composed of three uniform linear arrays with different spacings at specific distances is designed, and the analytical expression of its degrees of freedom is derived. The configuration rules of the array are concise, and the difference coarrays of the array are hole-free. Mathematically proves that all virtual array elements can be covered with a weight of not less than 3 except the 4 farthest. The array has lower sensors importance and less generalized k-fragility. The array design takes into account the maximum degrees of freedom, redundancy and sparsity. In case of sensors failure, compared with the other sparse linear arrays, the array has more stable difference coarrays and higher uniform degrees of freedom. Compared with multiple-fold redundancy arrays, the array has lower mutual coupling. Simulation results show that the ternary redundant array has superior robustness and higher DOA estimation accuracy under sensors failure.
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