Improved bearing and range estimation via high-order subspace based Unitary ESPRIT

Two new algorithms for the passive localization of near-field sources with a uniform linear array (ULA) are presented and compared in this paper. They exploit multiple invariances among certain fourth-order cross-cumulant matrices of non-zero and zero lags, respectively. Both algorithms utilize 2-D Unitary ESPRIT to obtain automatically paired bearing and range estimates. The resulting closed-form subspace-based methods incorporate forward-backward averaging, use efficient real-valued processing of the cumulant data, and outperform previously proposed high-order subspace-based algorithms for the localization of near-field sources. In the limit, the presented schemes also work for far-field sources.