Restoration of Complex Signals Distorted by Aliasing as a Result of Bandpass Sub-Nyquist Sampling
Vladislav Lesnikov, Tatiana Naumovich, Alexander Chastikov, Alexander Metelyov
Abstract
Vladislav Lesnikov, Tatiana Naumovich, Alexander Chastikov, Alexander Metelyov
Abstract
The purpose of this article is to develop a technique for recovering a complex signal distorted by aliasing as a result of sub-Nyquist bandpass sampling. The developed technique is based on the use of multichannel sampling. Its essence is that the signal is processed in several channels. All channels are analog-to-digital converted with different sampling rates, each of which leads to aliasing in its channel. For each channel, a system of equations is compiled that describes the corresponding aliasing. The joint solution of these systems of equations makes it possible to restore the distorted signal. Consideration in this article is limited only to first-order aliasing, in which no more than two aliases overlap at each frequency. This work is a continuation of the approach developed by the authors, called multichannel multirate sampling.
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The purpose of this article is to develop a technique for recovering a complex signal distorted by aliasing as a result of sub-Nyquist bandpass sampling. The developed technique is based on the use of multichannel sampling. Its essence is that the signal is processed in several channels. All channels are analog-to-digital converted with different sampling rates, each of which leads to aliasing in its channel. For each channel, a system of equations is compiled that describes the corresponding aliasing. The joint solution of these systems of equations makes it possible to restore the distorted signal. Consideration in this article is limited only to first-order aliasing, in which no more than two aliases overlap at each frequency. This work is a continuation of the approach developed by the authors, called multichannel multirate sampling.
Key concepts: Aliasing, Anti-aliasing filter, Sampling (signal processing), Nyquist–Shannon sampling theorem, Computer science, Nyquist frequency, SIGNAL (programming language), Anti-aliasing