Comparison of four super-resolution techniques for complex spectra or points estimation

In this work, we evaluate performances of four super-resolution techniques for estimating complex spectra or points under various scenarios. Suitable for resolving non-coherent signals, the two adaptive techniques (Root-MUSIC and ESPRIT) use multiple snapshots to acquire data covariance matrix, which can then be divided into signal subspace and noise subspace for estimating the desired complex parameters. While only utilizing one snapshot to estimate parameters, the two non-adaptive techniques (Matrix Pencil and MODE) are suitable to deal with coherent signals.