Recovering second-order statistics from compressive measurements
Document Type
Conference Proceeding
Publication Date
12-1-2011
Abstract
This paper focuses on the reconstruction of second order statistics of signals under a compressive sensing framework, which can be useful in many detection problems. More specifically, the focus is on general cyclostationary signals that are compressed using random linear projections, and using those compressive measurements, the cyclic power spectrum is retrieved. Subsequently, this can for instance be used to detect the occupation of specific frequency bands, which has applications in cognitive radio. Surprisingly, if the span of the random linear projections is larger than the period of the cyclostationary signals, the cyclic power spectrum can be recovered without putting any sparsity constraints on it, which allows for simple least squares reconstruction methods. This result indicates that significant compression can be realized by directly reconstructing the second-order statistics rather than the random signals themselves. © 2011 IEEE.
Publication Title
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Recommended Citation
Leus, G.,
&
Tian, Z.
(2011).
Recovering second-order statistics from compressive measurements.
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011, 337-340.
http://doi.org/10.1109/CAMSAP.2011.6136019
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10357