Linear-predictive data extrapolation narrow-band spectral estimation

1988 ◽  
Vol 76 (9) ◽  
pp. 1249-1251 ◽  
Author(s):  
D.N. Swingler ◽  
R.S. Walker
Keyword(s):  
Narrow Band ◽  
Spectral Estimation ◽  
Data Extrapolation

Clutter removal for measuring low-frequency narrow-band antenna using spectral estimation

2018 ◽  
Vol 11 (3) ◽  
pp. 215-219
Author(s):  
C. F. Hu ◽  
N. J. Li
Keyword(s):  
Time Domain ◽  
Measurement Accuracy ◽  
Narrow Band ◽  
Spectral Estimation ◽  
Low Frequency ◽  
Ar Model ◽  
Signal Processing Technique ◽  
Antenna Measurement ◽  
The Time Domain ◽  
Time Gating

AbstractThe measurement accuracy of low-frequency narrow-band antenna is heavily influenced by its environment, which is also difficult to remove the clutter with a time gating. This paper proposes a method to improve the measurement accuracy of low-frequency narrow-band antenna using signal processing technique. The method is to predict the unknown value out of received original signal with an auto-regressive model (AR model) based on modern spectral estimation theory, and the parameters in AR model are calculated by maximum entropy spectral estimation algorithm. Thus, a wideband signal compared with the original band is obtained, and then the time-domain resolution is enhanced. The time gating is more exactly to separate the antenna radiation signal from multipath signals. The simulation and experimental results show that about 50% extended data for each ends of original band can be obtained after spectral extrapolation, and the time-domain resolution after extrapolation is twice than the original narrow-band signal, and the influence of measurement environment can be eliminated effectively. The method can be used to improve accuracy in actual antenna measurement.

scholarly journals Accurate QTF Sensing Approach by Means of Narrow Band Spectral Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Graziella Scandurra ◽  
Gino Giusi ◽  
Carmine Ciofi
Keyword(s):  
Tuning Fork ◽  
Narrow Band ◽  
Spectral Estimation ◽  
Quartz Tuning Fork ◽  
Noise Spectrum ◽  
Operating Conditions ◽  
Single Measurement ◽  
New Approach ◽  
Tuning Forks ◽  
Noise Measurements

We propose a new approach for the extraction of the equivalent parameters of quartz tuning forks used as sensors by means of noise measurements. Noise is used as the test signal for the determination, by means of spectral analysis, of the frequency response of a circuit including the quartz tuning fork whose parameters need to be determined. A new approach for the analysis of strongly peaked noise spectra was developed in order to allow the correct measurement of the strongly peaked noise spectrum at the output of the system, which is the result of the high-quality factor of any quartz tuning fork-based sensor. With the approach we propose, the best compromise in terms of accuracy and measurement time can be obtained in a single measurement run. The performances of the approach we propose are discussed in comparison with those that can be obtained from a swept spectrum approach in the same operating conditions.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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