Software
(Subtitle: How to check the recovery performance of a given sensing matrix?)
- Block iterative reweighted algorithms for super-resolution of spectrally sparse signals
(Recovery of spectrally sparse signal from partial measurement/ Figuring out frequency locations in the continuous domain from partial time sampled data, or vise versa)
Novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals are introduced. Our iterative reweighted frequency recovery algorithms employ the support knowledge gained from earlier steps of our algorithms as block prior information to enhance frequency recovery. Our methods improve the performance of the atomic norm minimization which is a useful heuristic in recovering continuous-valued frequency contents. Numerical results demonstrate that our block iterative reweighted methods provide both better recovery performance and faster speed than other known methods.
Prerequisite software (or solver) to run the MATLAB code: CVX
(Finding abnormal random variable out of normal random variable from observations)
(Extracting exact frequency locations in the continuous domain from only partial magnitude observations in the time domain)
(Direction of Arrival (DoA) estimation with auto-sensor calibration)