Software

- Computable performance guarantees for compressed sensing matrices

(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)

- Compressed hypothesis testing: To mix or not to mix?

(Finding abnormal random variable out of normal random variable from mixed observations)

- Phaseless Super-resolution in the continuous domain

(Extracting exact frequency locations in the continuous domain from only partial magnitude observations in the time domain)

Phaseless super-resolution refers to the problem of super-resolving a signal from only its low-frequency Fourier magnitude measurements. In this paper, we consider the phaseless super-resolution problem of recovering a sum of sparse Dirac delta functions which can be located anywhere in the continuous time-domain. For such signals in the continuous do- main, we propose a novel Semidefinite Programming (SDP) based signal recovery method to achieve the phaseless super-resolution in the continuous domain.

• Prerequisite software (or solver) to run the MATLAB code: CVX

[CODE] [PAPER]

- A non-convex approach to joint sensor calibration and spectrum estimation

(Direction of Arrival (DoA) estimation with auto-sensor calibration)