Gu Dazhen, Rezac Jacob, Lu Xifeng, Kuester Daniel
Shared Spectrum Metrology Group, National Institute of Standards and Technology, Boulder, CO 80305 USA.
High-Speed Waveform Metrology Group, National Institute of Standards and Technology, Boulder, CO 80305 USA.
IEEE Trans Instrum Meas. 2024;73. doi: 10.1109/tim.2024.3458037.
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.
我们展示了一项关于从时域采样数据中估计功率谱密度（PSD）的研究。这项工作是由我们最近在数字辐射测量方面的进展所推动的，在数字辐射测量中，通过处理数字采样信号来获得辐射光谱。PSD估计可以通过二次估计器进行推广，并且估计器的均方误差最小化会导致最优窗口选择。为了量化数字信号处理中与非理想PSD估计相关的不确定性，我们制定了方差和偏差的界限。我们给出了频谱测量的加窗估计，以便在计算效率和幅度测量精度方面进行比较。还展示了一些真实数据和模拟数据的例子以供比较。
