Direction-of-Arrival Estimation using a Direct-Data Approach

This paper presents a direct-data (DD) counterpart to the covariance-based (CB) algorithm for direction-of-arrival (DOA) estimation. The proposed DD-DOA scheme provides reduced computational complexity as compared with other ESPRIT variations, filling in a theoretical gap not covered by previously presented schemes. A mean-squared error (MSE) analysis as well as computer simulations are provided allowing performance comparisons between DD-DOA and ESPRIT algorithms. The MSE performance is compared with the Cramer-Rao lower bound (CRLB) for one source. Results indicate that the proposed algorithm represents an efficient trade-off between computational complexity and final MSE as compared with standard ESPRIT schemes.