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PUBLICATIONS

On Digital Signal Processing of Time Series for Spectrum Estimation

Published

Author(s)

Dazhen Gu, Jake Rezac, Xifeng Lu, Dan Kuester

Abstract

We present a study of power spectral density (PSD) estimation from data sampled in the time domain. This work was motivated by our recent development of digital radiometry, where radiation spectra were obtained by processing the digitally sampled signal. The PSD estimation can be generalized by a quadratic estimator and minimization of mean squared error of the estimator leads to the optimal window choice. The bounds of the variance and the bias are formulated in order to quantify the uncertainty associated with non-ideal PSD estimation in digital signal processing. Windowed estimates of spectrum measurements are presented for comparison in terms of computational efficiency and amplitude measurement precision. A few examples on real and simulated data are shown for comparison.
Citation
IEEE Transactions on Instrumentation and Measurement
Pub Type
Journals

Download Paper

Local Download
Metrology and Electrical / electromagnetic metrology

Citation

Gu, D. , Rezac, J. , Lu, X. and Kuester, D. (2024), On Digital Signal Processing of Time Series for Spectrum Estimation, IEEE Transactions on Instrumentation and Measurement, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956831 (Accessed October 22, 2025)

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Created September 11, 2024
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