基于改进典范分解的嵌套阵列DOA估计

摘 要：为了避免在对嵌套阵列做DOA估计时进行空域峰值搜索，将典范分解应用于嵌套阵列，通过单次奇异值分解、双线性映射和张量分解得到阵列导向量矩阵和入射角。但现有典范分解算法只适用于无噪条件，通过两次奇异值分解改进了该算法，使其在无噪和有噪条件下均适用。仿真结果表明：在相同信噪比、快拍数情况下，基于改进典范分解的嵌套阵列DOA估计算法相比MUSIC、空间平滑算法具有更好的估计性能和更少的运算时间。

Improved CPD based DOA estimation of nested array

In order to avoid searching the peak value in space domain, when estimating nested array’s the direction of direction of arrival(DOA), the canonical polyadic decomposition(CPD) was applied into the nested array, namely using the one time singular value decomposition(SVD), bilinear mapping and tensor decomposition to obtain the steering vector matrix and arrival angle.However, the existing CPD algorithm only can be applied in noiseless environment, the algorithm was improved by utilizing SVD two times, and was made to be applied in both noiseless and noisy environments.The simulation results demonstrate that in the same signal to noise ratio(SNR) and snapshot, the DOA estimation algorithm of nested array based on the improved CPD has better performances and less running time than the MUSIC and space smoothing algorithms.