NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
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
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 2, 2025)