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09/11/2024 | Press release | Distributed by Public on 09/12/2024 03:09
On Digital Signal Processing of Time Series for Spectrum Estimation
Published
September 11, 2024
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
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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 September 12, 2024)
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