High Resolution Direction Finding Using Krylov Subspace

This paper proposes two new algorithms for the direction of arrival (DOA) estimation of P radiating sources. Unlike the classical subspace-based methods, they do not resort to the eigen-decomposition of the covariance matrix of the received data. Indeed, the proposed algorithms involve the building of the signal subspace from the Krylov subspace of order P associated with the covariance matrix of the received data and a search steering vector, through either the multi-stage Wiener filter (MSWF) or the conjugate gradient method (CG). The proposed algorithms exhibit a higher super-resolution capability than the classical MUSIC and ESPRIT algorithms. A comparison with another theoretically equivalent Krylov subspace-based algorithm, namely the auxiliary vector basis is also presented. It reveals that the proposed CG-based method outperforms over its counterparts in term of resolution of closely spaced-sources with a small number of snapshots and a low signal-to-noise ratio (SNR).