scholarly journals Multitaper spectral analysis of atmospheric radar signals

2004 ◽  
Vol 22 (11) ◽  
pp. 3995-4003 ◽  
Author(s):  
V. K. Anandan ◽  
C. J. Pan ◽  
T. Rajalakshmi ◽  
G. Ramachandra Reddy
Keyword(s):  
Spectral Analysis ◽  
Lower Stratosphere ◽  
Signal To Noise Ratio ◽  
Power Spectra ◽  
Estimation Accuracy ◽  
Doppler Width ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Width Measurements ◽  
Backscattered Signals

Abstract. Multitaper spectral analysis using sinusoidal taper has been carried out on the backscattered signals received from the troposphere and lower stratosphere by the Gadanki Mesosphere-Stratosphere-Troposphere (MST) radar under various conditions of the signal-to-noise ratio. Comparison of study is made with sinusoidal taper of the order of three and single tapers of Hanning and rectangular tapers, to understand the relative merits of processing under the scheme. Power spectra plots show that echoes are better identified in the case of multitaper estimation, especially in the region of a weak signal-to-noise ratio. Further analysis is carried out to obtain three lower order moments from three estimation techniques. The results show that multitaper analysis gives a better signal-to-noise ratio or higher detectability. The spectral analysis through multitaper and single tapers is subjected to study of consistency in measurements. Results show that the multitaper estimate is better consistent in Doppler measurements compared to single taper estimates. Doppler width measurements with different approaches were studied and the results show that the estimation was better in the multitaper technique in terms of temporal resolution and estimation accuracy.

scholarly journals NOISE STABILITY OF SIGNAL RECEPTION ALGORITHMS WITH MULTIPULSE PULSE-POSITION MODULATION

2018 ◽  
Vol 42 (1) ◽  
pp. 167-174 ◽  
Author(s):  
V. I. Parfenov ◽  
D. Y. Golovanov
Keyword(s):  
Communication Systems ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Point Of View ◽  
Estimation Accuracy ◽  
Pulse Position Modulation ◽  
Signal To Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Ratio

An algorithm for estimating time positions and amplitudes of a periodic pulse sequence from a small number of samples was proposed. The number of these samples was determined only by the number of pulses. The performance of this algorithm was considered on the assumption that the spectrum of the original signal is limited with an ideal low-pass filter or the Nyquist filter, and conditions for the conversion from one filter to the other were determined. The efficiency of the proposed algorithm was investigated through analyzing in which way the dispersion of estimates of time positions and amplitudes depends on the signal-to-noise ratio and on the number of pulses in the sequence. It was shown that, from this point of view, the efficiency of the algorithm decreases with increasing number of sequence pulses. Besides, the efficiency of the proposed algorithm decreases with decreasing signal-to-noise ratio.It was found that, unlike the classical maximum likelihood algorithm, the proposed algorithm does not require a search for the maximum of a multivariable function, meanwhile characteristics of the estimates are practically the same for both these methods. Also, it was shown that the estimation accuracy of the proposed algorithm can be increased by an insignificant increase in the number of signal samples.The results obtained may be used in the practical design of laser communication systems, in which the multipulse pulse-position modulation is used for message transmission. 

The Noise Analysis of Fluid Systemsin Solid-State Nanopore Sensors

2013 ◽  
Vol 419 ◽  
pp. 517-520 ◽  
Author(s):  
Song Ying ◽  
Lei Wang ◽  
Wen Yuan Zhao
Keyword(s):  
Solid State ◽  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Power Spectra ◽  
Noise Power ◽  
Label Free ◽  
Signal To Noise ◽  
Fluid Systems ◽  
Noise Ratio ◽  
Nanopore Sensors

The solid-state nanopore sensor offers a versatile platform for the rapid, label-free electrical detection and analysis of single molecules, especially on DNA sequencing. However, the overall signal-to-noise ratio (SNA) is a major challenge in sequencing applications. In our work, two different fluid systems made by metal and plexiglass have been designed to improve the signal to noise ratio of the solid-state nanopore sensor. From the measurements on the noise power spectra with a variety of conditions, it is found that plexiglass fluid system coupled with shielding box produces a good quality of electric signals on nanopore sensors.

scholarly journals Signal to Noise Ratio Characterization of Coherent Doppler Lidar Backscattered Signals

2016 ◽  
Vol 119 ◽  
pp. 17014 ◽  
Author(s):  
Sameh Abdelazim ◽  
David Santoro ◽  
Mark Arend ◽  
Fred Moshary ◽  
Sam Ahmed
Keyword(s):  
Signal To Noise Ratio ◽  
Doppler Lidar ◽  
Signal To Noise ◽  
Coherent Doppler Lidar ◽  
Noise Ratio ◽  
Backscattered Signals

scholarly journals A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Luxi Yang ◽  
Xinyu Zhang
Keyword(s):  
Prior Information ◽  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Signal To Noise ◽  
Probability Hypothesis Density ◽  
Localization And Mapping ◽  
Noise Ratio

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

scholarly journals Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection

2022 ◽  
Vol 12 (2) ◽  
pp. 826
Author(s):  
Jing Yuan ◽  
Bo Yu ◽  
Changxiang Yan ◽  
Junqiang Zhang ◽  
Ning Ding ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Spectral Resolution ◽  
Soil Moisture Content ◽  
Signal To Noise Ratio ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Transfer Model ◽  
Signal To Noise ◽  
Noise Ratio

It is found that the remote sensing parameters such as spectral range, spectral resolution and signal-to-noise ratio directly affect the estimation accuracy of soil moisture content. However, the lack of research on the relationship between the parameters and estimation accuracy restricts the prolongation of application. Therefore, this study took the demand for this application as the foothold for developing spectrometry. Firstly, a method based on sensitivity analysis of soil radiative transfer model-successive projection algorithm (SA-SPA) was proposed to select sensitive wavelengths. Then, the spectral resampling method was used to select the best spectral resolution in the corresponding sensitive wavelengths. Finally, the noise-free spectral data simulated by the soil radiative transfer model was added with Gaussian random noise to change the signal-to-noise ratio, so as to explore the influence of signal-to-noise ratio on the estimation accuracy. The research results show that the estimation accuracy obtained through the SA-SPA (RMSEP < 12.1 g kg−1) is generally superior to that from full-spectrum data (RMSEP < 14 g kg−1). At selected sensitive wavelengths, the best spectral resolution is 34 nm, and the applicable signal-to-noise ratio ranges from 150 to 350. This study provides technical support for the efficient estimation of soil moisture content and the development of spectrometry, which comprehensively considers the common influence of spectral range, spectral resolution and signal-to-noise ratio on the estimation accuracy of soil moisture content.

scholarly journals Accuracy of Shack–Hartmann wavefront sensor using a coherent wound fibre image bundle

2018 ◽  
Vol 35 ◽  
Author(s):  
Jessica R. Zheng ◽  
Michael Goodwin ◽  
Jon Lawrence
Keyword(s):  
Measurement Accuracy ◽  
Signal To Noise Ratio ◽  
Optical Systems ◽  
Wavefront Sensor ◽  
Estimation Accuracy ◽  
Signal To Noise ◽  
Centroid Method ◽  
Structure Defects ◽  
Noise Ratio ◽  
Bundle Structure

AbstractShack–Hartmannwavefront sensors using wound fibre image bundles are desired for multi-object adaptive optical systems to provide large multiplex positioned by Starbugs. The use of a large-sized wound fibre image bundle provides the flexibility to use more sub-apertures wavefront sensor for ELTs. These compact wavefront sensors take advantage of large focal surfaces such as the Giant Magellan Telescope. The focus of this paper is to study the wound fibre image bundle structure defects effect on the centroid measurement accuracy of a Shack–Hartmann wavefront sensor. We use the first moment centroid method to estimate the centroid of a focused Gaussian beam sampled by a simulated bundle. Spot estimation accuracy with wound fibre image bundle and its structure impact on wavefront measurement accuracy statistics are addressed. Our results show that when the measurement signal-to-noise ratio is high, the centroid measurement accuracy is dominated by the wound fibre image bundle structure, e.g. tile angle and gap spacing. For the measurement with low signal-to-noise ratio, its accuracy is influenced by the read noise of the detector instead of the wound fibre image bundle structure defects. We demonstrate this both with simulation and experimentally. We provide a statistical model of the centroid and wavefront error of a wound fibre image bundle found through experiment.

THE COEFFICIENT OF COHERENCE: ITS ESTIMATION AND USE IN GEOPHYSICAL DATA PROCESSING

Geophysics ◽  
1967 ◽  
Vol 32 (4) ◽  
pp. 602-616 ◽  
Author(s):  
M. R. Foster ◽  
N. J. Guinzy
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Power Spectra ◽  
Stationary Time Series ◽  
Signal To Noise ◽  
Spectral Window ◽  
Data Window ◽  
Approximate Distribution ◽  
Noise Ratio ◽  
Gaussian Data

The coefficient of coherence between two stationary time series was introduced by Wiener in 1930. It is related to the signal‐to‐noise ratio, to the minimum prediction error, and has important invariance properties. As an estimate of this parameter, most geophysicists have used the so‐called “sample coherence.” An approximate distribution of the sample coherence for Gaussian data has been derived by N. R. Goodman. We have tested this distribution by means of Monte Carlo experiments for validity and robustness (insensitivity to the Gaussian assumption). It has passed the tests. The Goodman distribution provides a means of constructing estimates of the true coherence which are better than the widely used sample coherence. It can also be used to calculate confidence intervals. Finally, it forms a basis for choosing the lag window and data window necessary for best estimation of the true coherence. For good estimates of the true coherence, two precautions must be observed: 1. The cross‐spectrum and power spectra of the two time series must be smoothly varying over the width of the spectral window. 2. The ratio of the length of the data window to the lag window must be large. For most seismic work the second requirement severely limits the spectral resolution. Examples show that large errors can result if this resolution is not sufficient to satisfy the first requirement. In many geophysical studies the parameter of interest is the signal‐to‐noise ratio. Because of its relation to the coherence, the Goodman distribution provides a basis for its estimation as well.

Sign in / Sign up

Export Citation Format

Share Document