A novel method for two-dimensional frequency estimation

This paper proposes a novel solution to two-dimensional (2-D) frequency estimation problems. The solution is applicable to the cases where the data length is much larger in one dimension than the other. The method estimates 2-D frequencies based on several one-dimensional (1-D) frequency estimation processes and hence has a low computational complexity. To avoid resolving close frequencies in 1-D processing, we construct matrices to estimate the linear combinations of the 2-D frequencies. Performance evaluation of this method is presented based on the comparison of the Crame´r-Rao bound (CRB) and numerical simulations.