An eigenvector-based algebraic approach for two-dimensional frequency estimation with improved identifiability

This paper presents an algebraic method for two-dimensional (2-D) frequency estimation. The algorithm is based on multidimensional smoothing and data folding, and offers significantly improved identifiability than existing algebraic approaches, thus is termed the improved multidimensional folding (IMDF) algorithm. It is shown that with IMDF, up to approximately 0.34 M₁M₂ 2-D frequencies can be uniquely resolved with probability one from an M₁ by M₂ data mixture. The IMDF algorithm achieves automatic frequency N-dimensional frequency estimation. Simulation results demonstrate its competitive performance compared to some existing algebraic methods.