2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-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
SessionSPTM-6: Sampling, Multirate Signal Processing and Digital Signal Processing 2
LocationGather.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
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.