| Paper ID | SPTM-6.3 |
| Paper Title |
COMPRESSIVE SIGNAL RECOVERY UNDER SENSING MATRIX ERRORS COMBINED WITH UNKNOWN MEASUREMENT GAINS |
| Authors |
Jian Vora, Ajit Rajwade, Indian Institute of Technology, Bombay, India |
| Session | SPTM-6: Sampling, Multirate Signal Processing and Digital Signal Processing 2 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Compressed Sensing assumes a linear model for acquiring signals however imperfections may arise in the specification of the `ideal' measurement model. We present the first study which considers the case of two such common calibration issues: (a) unknown measurement scaling (sensor gains) due to hardware vagaries or due to unknown object motion in MRI scanning, \emph{in conjunction with} (b) unknown offsets to measurement frequencies in case of a Fourier measurement matrix. We propose an alternating minimisation algorithm for on-the-fly signal recovery in the case when errors (a) and (b) occur \emph{jointly}. We show simulation results over a variety of situations that outperform the baselines of signal recovery by ignoring either or both types of calibration errors. We also show theoretical results for signal recovery by introducing a perturbed version of the well-known Generalized Multiple Measurement Vectors (GMMV) model. |
