### Abstract

Language | English |
---|---|

Number of pages | 15 |

Journal | IEEE Transactions on Instrumentation and Measurement |

DOIs | |

State | Accepted/In press - 2019 |

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### Cite this

*IEEE Transactions on Instrumentation and Measurement*. DOI: 10.1109/TIM.2019.2902023

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*IEEE Transactions on Instrumentation and Measurement*. DOI: 10.1109/TIM.2019.2902023

**Quick estimation of periodic signal parameters from 1-bit measurements.** / Carbone, Paolo (Corresponding author); Schoukens, Johan; Moschitta, Antonio.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Quick estimation of periodic signal parameters from 1-bit measurements

AU - Carbone,Paolo

AU - Schoukens,Johan

AU - Moschitta,Antonio

PY - 2019

Y1 - 2019

N2 - Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. Although several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is, binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is, the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to the published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function but provides solutions that are very close to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted.

AB - Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. Although several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is, binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is, the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to the published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function but provides solutions that are very close to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted.

U2 - 10.1109/TIM.2019.2902023

DO - 10.1109/TIM.2019.2902023

M3 - Article

JO - IEEE Transactions on Instrumentation and Measurement

T2 - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

ER -