A Low-Complexity Nyström-Based Algorithm for Array Subspace Estimation

Subspace-based methods rely on singular value decomposition (SVD) of the sample covariance matrix (SCM) to compute the array signal or noise subspace. For large array, triditional subspace-based algorithms inevitably lead to intensive computational complexity due to both calculating SCM and performing SVD of SCM. To circumvent this problem, a NyströmBased algorithm for array subspace estimation is proposed in this paper. In the proposed algorithm, we construct an approximated rank-k SVD of SCM without computing SCM, leading to computational simplicity. Statistical analysis and simulation results show that the rank-k SVD signal-subspace estimation algorithm (RKSSE) is computationally simple.