Off-Grid sparse blind sensor calibration

Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal is sparse and has support only on the discretized grid points. Due to continuous nature of the signals, representing the signal on a discretized grid results in the off-grid problem. Improper calibration is also another issue which can cause performance degradation. In this paper, a CS based blind calibration method is proposed for the multiple off-grid signal case. Proposed method is capable of estimating the off-grid signal parameters and correcting the gain and the phase errors simultaneously. Simulation analysis is performed and comments are drawn. Results show that the proposed method have superior performance in terms of the calculated metrics.

[1]  Rémi Gribonval,et al.  A conjugate gradient algorithm for blind sensor calibration in sparse recovery , 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

[2]  Orhan Arikan,et al.  Autofocused Spotlight SAR Image Reconstruction of Off-Grid Sparse Scenes , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Haibin Ling,et al.  An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Michael Unser,et al.  Autocalibrated signal reconstruction from linear measurements using adaptive GAMP , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  B. C. Ng,et al.  Sensor-array calibration using a maximum-likelihood approach , 1996 .

[6]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[7]  Rémi Gribonval,et al.  Convex Optimization Approaches for Blind Sensor Calibration Using Sparsity , 2013, IEEE Transactions on Signal Processing.

[8]  Stefan J. Wijnholds,et al.  Blind calibration of phased arrays using sparsity constraints on the signal model , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[9]  Laurent Daudet,et al.  Room Reverberation Reconstruction: Interpolation of the Early Part Using Compressed Sensing , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Laura Balzano,et al.  Robust blind calibration via total least squares , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[12]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[13]  L. Balzano,et al.  Blind Calibration of Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.