Efficient eigenspace-based array signal processing using multiple shift-invariant subarrays

This paper deals with the construction of eigensubspaces for adaptive array signal processing. An efficient technique for extracting the eigensubspaces spanned by the data vector received by an N-element adaptive array is presented. We first decompose the original array into several subarrays with multiple shift invariances and find the eigensubspaces corresponding to each of the subarrays. By solving a least-squares (LS) or total least-squares (TLS) problem, the signal and noise subspaces corresponding to the original array can be found from the eigensubspaces spanned by the subarray data vectors. Hence, there is no need to perform the eigenvalue decomposition of the N×N correlation matrix of the received data vector. The proposed technique significantly reduces the required computational complexity as compared to the conventional eigenspace-based (ESB) methods. In conjunction with the spatial smoothing scheme or a proposed cross-correlation method, this technique can also deal with the case of coherent signals. The effectiveness of the proposed technique is demonstrated by several computer simulations