Radar and Array Processing: Sub-Nyquist Sampling of Correlated Signals
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- December 27, 2021
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Digital computation is deeply ingrained in modern signal processing, and an ecient analogue-to-digital conversion is of fundamental importance. This work proposes novel sampling schemes for the ecient conversion from an analog-to-digital representation of a sparse and/or correlated (S&C) signal ensemble. An S&C ensemble consists of multiple signals well approximated by the linear combinations a few latent spectrally-sparse signals. Such ensembles arise in various applications in array processing, where it is easy to nd thousands of signals possibly spanning large swaths of bandwidths but with relatively much fewer hidden degrees of freedom. Acquiring such signals plainly at Nyquist rate produces humongous amount of digital data every second, and also requires expensive fast-rate analog-to-digital converters (ADC). To address these issues, an immediate challenge is the design of an adaptive and ecient sampling scheme that can acquire S&C ensemble using low-rate ADCs by taking advantage of latent sparse, and correlated signal structure in the ensemble. For this purpose, we design a sampling architecture, that uses simple and-easy-to-implement analog components such as switches, and integrators for the preprocessing of signals. Each signal is then compressively sampled using a low-rate ADC.
Applications
Array processing, Neuronal recordings from brain tissues.
Related Papers
[1] A. Ahmed, and J. Romberg Compressive multiplexing of correlated signals., Accepted in IEEE Transaction on Information Theory, 2014.
[2] A. Ahmed Compressive Acquisition and Least-Squares Reconstruction of Correlated Signals, Accepted in IEEE Signal Processing Letters, 2017.
[3] A. Ahmed, and J. Romberg Compressive multiplexers for correlated signals, Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012.
[4] A. Ahmed, and J. Romberg Compressive sampling in array processing, In IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013.
[5] A. Ahmed, and F. Shamshad Compressive Sampling of Sparse and Correlated Signals, submitted, 2019.
[6] A. Ahmed, and F. Shamshad Compressive Sampling and Least Squares based Reconstruction of Correlated Signals., Accepted in International Conference on Sampling Theory and Applications (SampTA), 2019.