An efficient algorithm for two-dimensional frequency estimation

This paper presents a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. We derive the theoretical performance of the 2D-MODE estimator and show that it is asymptotically statistically efficient under either the assumption that the number of temporal snapshots is large or the signal-to-noise ratio is high. Numerical examples showing the performance of this algorithm and comparing it with the computationally efficient subspace rotation algorithms are also given. We show that the statistical performance of the 2D-MODE algorithm is better than that of the subspace rotation methods. The amount of computations required by the former is no more than a few times of that needed by the latter for either small numbers of spatial measurements or a single temporal snapshot, which are the cases of interest herein.