Mass Sensitivity or Gravimetric Satellites

2020 ◽  
Author(s):  
Robert Spero
Keyword(s):  
Signal To Noise Ratio ◽  
Satellite Tracking ◽  
Point Mass ◽  
Laser Ranging ◽  
Signal To Noise ◽  
Orbital Parameters ◽  
The Earth ◽  
Noise Ratio ◽  
Optimal Signal ◽  
Instrument Sensitivity

<p class="p1">A point mass on the surface of the Earth gives the highest frequency content for orbiting gravimetry, with<span class="Apple-converted-space">  </span>the maximum frequency for gradiometers or satellite-to-satellite tracking determined by orbital altitude.  Frequency-domain expressions are found for<span class="Apple-converted-space">  </span>measurements of a point-like source on the surface of the Earth.<span class="Apple-converted-space">  </span>The response of orbiting gradiometers such as GOCE and satellite-to-satellite tracking missions such as GRACE-FO are compared. The optimal signal-to-noise ratio as a function<span class="Apple-converted-space">  </span>of noise in the measurement apparatus is computed, and from that the minimum detectable mass is inferred. The point mass magnitude that gives signal-to-noise ratio = 3 is for GOCE<span class="Apple-converted-space">  </span>M_3=200 Gton and<span class="Apple-converted-space">  </span>for the laser ranging interferometer measurement on GRACE-FO<span class="Apple-converted-space">  </span>M_3= 0.5 Gton. For the laser ranging interferometer measurement, the optimal filter for detecting point-like masses has a passband of 1 to 20 mHz,<span class="Apple-converted-space">  </span>differing from the 0.3 to 20 mHz admittance filter of Ghobadi-Far et al. (2018), which is not specialized for detecting point-like masses. M_3 for<span class="Apple-converted-space">  </span>future GRACE-like missions with different orbital parameters and improved instrument sensitivity is explored, and the optimum spacecraft separation is found.</p>

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

scholarly journals Optimal signal-to-noise ratio for silicon nanowire biochemical sensors

2011 ◽  
Vol 98 (26) ◽  
pp. 264107 ◽  
Author(s):  
Nitin K. Rajan ◽  
David A. Routenberg ◽  
Mark A. Reed
Keyword(s):  
Signal To Noise Ratio ◽  
Silicon Nanowire ◽  
Signal To Noise ◽  
Biochemical Sensors ◽  
Noise Ratio ◽  
Optimal Signal

Coincidence Photon-Counting Laser Ranging for Moving Targets With High Signal-to-Noise Ratio

2014 ◽  
Vol 26 (15) ◽  
pp. 1495-1498 ◽  
Author(s):  
Zeyu Bao ◽  
Zhaohui Li ◽  
Yafan Shi ◽  
E. Wu ◽  
Guang Wu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Laser Ranging ◽  
High Signal ◽  
Moving Targets ◽  
Signal To Noise ◽  
Noise Ratio

High precision linear frequency modulation ranging system based on semiconductor laser

2018 ◽  
Vol 32 (11) ◽  
pp. 1850136 ◽  
Author(s):  
Mingyuan Xue ◽  
Cunxiao Gao ◽  
Linquan Niu ◽  
Shaolan Zhu ◽  
Na Guo ◽  
...  
Keyword(s):  
Semiconductor Laser ◽  
High Precision ◽  
Frequency Modulation ◽  
Signal To Noise Ratio ◽  
Linear Frequency Modulation ◽  
Repetition Frequency ◽  
Laser Ranging ◽  
Signal To Noise ◽  
Linear Frequency ◽  
Noise Ratio

Based on the linear frequency modulation (LFM) of semiconductor laser, a laser ranging system is built. The ranging precision of the system is about micron dimension with the repetition frequency of 100 kHz and a distance of 1–10 m. We analyzed the ranging precision experimentally. As the bandwidth increases, the ranging precision becomes higher and higher. When the bandwidth is fixed, as the distance increases, the signal-to-noise ratio (SNR) will decrease, which reduces the precision.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

Proposition of deposition and bias conditions for optimal signal-to-noise-ratio in resistor- and FET-type gas sensors

Nanoscale ◽  
2020 ◽  
Vol 12 (38) ◽  
pp. 19768-19775 ◽  
Author(s):  
Wonjun Shin ◽  
Gyuweon Jung ◽  
Seongbin Hong ◽  
Yujeong Jeong ◽  
Jinwoo Park ◽  
...  
Keyword(s):  
Gas Sensors ◽  
Signal To Noise Ratio ◽  
Optimal Performance ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

Response alone cannot fully evaluate the performance of sensors, and the signal-to-noise-ratio should additionally be considered to design gas sensors with optimal performance.

scholarly journals The robustness of the differential quantizer in the case of the variable signal to noise ratio

2017 ◽  
Vol 14 (1) ◽  
pp. 149-160
Author(s):  
Lazar Cokic ◽  
Aleksandra Marjanovic ◽  
Sanja Vujnovic ◽  
Zeljko Djurovic
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Fixed Number ◽  
Signal To Noise ◽  
Input And Output ◽  
Noise Ratio ◽  
The Difference ◽  
Optimal Signal ◽  
Theoretical Overview

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

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