In this article, we present a novel autocorrelation-based frequency estimation algorithm for single-tone sinusoidal signals. In comparison to other state-of-the-art frequency estimation methods, the proposed one provides a better tradeoff between accuracy, complexity, and estimation range. In particular, the algorithm is able to achieve the Cramer–Rao lower bound for moderate and high signal-to-noise ratio, and its implementation is feasible even in resource-constrained microcontrollers, as those commonly used in the Industrial Internet of Things (IIoT) applications and low-cost instrumentation. Finally, we investigate the performance of the algorithm in the case of a practical IIoT application, i.e., frequency estimation of unbalanced three-phase power systems, showing that it outperforms several other autocorrelation-based estimators.

A Novel Low-complexity Frequency Estimation Algorithm for Industrial Internet of Things Applications

Giuseppe Campobello
Primo
;
Antonino Segreto
Secondo
;
Nicola Donato
Ultimo
2020-01-01

Abstract

In this article, we present a novel autocorrelation-based frequency estimation algorithm for single-tone sinusoidal signals. In comparison to other state-of-the-art frequency estimation methods, the proposed one provides a better tradeoff between accuracy, complexity, and estimation range. In particular, the algorithm is able to achieve the Cramer–Rao lower bound for moderate and high signal-to-noise ratio, and its implementation is feasible even in resource-constrained microcontrollers, as those commonly used in the Industrial Internet of Things (IIoT) applications and low-cost instrumentation. Finally, we investigate the performance of the algorithm in the case of a practical IIoT application, i.e., frequency estimation of unbalanced three-phase power systems, showing that it outperforms several other autocorrelation-based estimators.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3203735
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