Power Spectral Density for Constrained Long Wavelength Guideway Irregularities

1978 ◽  
Vol 100 (1) ◽  
pp. 18-23 ◽  
Author(s):  
M. Bala Krishna ◽  
David Hullender
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Upper Bound ◽  
Linear Modeling ◽  
Random Inputs ◽  
Long Wavelength ◽  
Power Spectral ◽  
Modeling Techniques ◽  
Insight Into

An equation for the power spectral density (PSD) of guideway irregularities that have been constrained to lie within a designated band is formulated. The equation enables guideway designers to control the upper bound on the long wavelength portion of the roughness PSD. The paper also provides insight into the accuracy of two quasi-linear modeling techniques for nonlinearities with random inputs.

scholarly journals Bound on the amplitude of the Earth's free core–nutation

1988 ◽  
Vol 128 ◽  
pp. 293-299 ◽  
Author(s):  
T. A. Herring ◽  
C. R. Gwinn ◽  
B. A. Buffett ◽  
I. I. Shapiro
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Upper Bound ◽  
Confidence Limit ◽  
Temporal Variations ◽  
Weather Data ◽  
Free Core Nutation ◽  
Long Baseline ◽  
Power Spectral ◽  
Vlbi Data

We analyzed six years of very–long–baseline interferometry (VLBI) data and determined corrections to the coefficients of the seven terms with the largest amplitudes in the IAU 1980 nutation series. Our analysis yields results consistent with earlier analyses of smaller sets of VLBI data, within the uncertainties of the latter. Here, we restrict discussion to the freely excited core–nutation or “free core–nutation” (FCN). Our analysis yields an estimate of 0.33 ± 0.12 mas for an assumed constant amplitude of the FCN, which allows us to place an upper bound on it of 0.6 mas (99.5% confidence limit). We also studied possible temporal variations of the complex amplitude of the FCN by modeling it as a stochastic process with a white noise excitation. We detected no statistically significant variations of this amplitude for the six–year interval spanned by the VLBI data. However, in the neighborhood of one cycle per day, the power spectral density of the atmospheric surface loading is estimated from global weather data to be 0.24 (g cm−2)2 day, about five times larger than the largest such power spectral density that would be consistent with the upper bound on the amplitude of the FCN placed by the VLBI data. Thus, we conclude that this estimate is too high and that, if the FCN were excited by surface loads with frequencies near one cycle per day, then the power spectral density of these loads must be <0.06 (g cm−2)2 day (99.9% confidence limit).

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral

scholarly journals Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Spectral Density ◽  
Noise Reduction ◽  
Power Spectral Density ◽  
Sound Source ◽  
Processing Algorithm ◽  
Post Processing ◽  
Time Frequency ◽  
Distance Error ◽  
Rotor Noise ◽  
Power Spectral

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

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