Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Imrich Andráš; Pavol Dolinský; Linus Michaeli; Ján Šaliga

doi:10.1515/msr-2018-0025

Abstract

This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

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Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

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