Two-Dimensional DOA Estimation Based on L-shaped Sum Difference Nested Array

In order to solve the problems that the number of estimable sources is limited by the number of elements and low resolution in the estimation of the direction of arrival （DOA） of the traditional L-shaped uniform array， this paper proposes a new L-shaped sum difference nested array structure. The two sub-arrays of the L-shaped array have the same layout， which is a non-uniform sparse array. The effect of virtual expansion of the number of array elements is achieved through the difference and sum operation between the positions of the array elements， which improves the freedom of the array. When using this array for two-dimensional DOA estimation， the two sub-arrays first perform one-dimensional DOA estimation， and then use the PSCM （Pair-matching Signal Covariance Matrices） algorithm for one-dimensional angle pairing. When the one-dimensional direction of arrival estimation of each sub-array is performed. Firstly， the VCAM （Vectorized Conjugate Augmented MUSIC） algorithm is used to generate the summation and difference covariance matrix of the non-uniform sparse matrix. Second， the matrix reconstruction method is used to restore the rank of the covariance matrix. Finally， the MUSIC （Multiple Signal Classification） algorithm is used to estimate the DOA of the covariance matrix. Experimental simulation shows that this array has higher degrees of freedom and estimation accuracy.