<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to strengthen the applicability of data denoising algorithm, this thesis study the common telemetry data denoising algorithm based on the data of engine speed, flight space speed, cabin temperature and humidity, and establishes the evaluation model of error square sum and curve similarity to evaluate the denoising performance. Experiments show that the polynomial fitting has the greatest denoising error and slow convergence speed. The five-point cubic smoothing has the smallest overall denoising error, the median filtering algorithm can change the effect of smoothing effect by adjust it's moothing window, but ignores the authenticity of data. Therefore, the above three data denoising algorithms do not meet the requirements of telemetry data processing. In this thesis, an improved threshold function is proposed which effectively improves the data jump and excessive smoothing and reduce the denoising accuracy compared with the traditional thresholding function in order to makes the measured value closer to the true value. The algorithm is applied to the noise processing of four kinds of telemetry data, the results show that the denoising accuracy is improved significantly compared with the other three algorithms, which makes the measured value closer to the true value to reflect the changing trend of the original measurement data more truthfully, and the curve similarity is improved significantly, which are all above 80%.</p>
</abstract>

In order to strengthen the applicability of data denoising algorithm, this thesis study the common telemetry data denoising algorithm based on the data of engine speed, flight space speed, cabin temperature and humidity, and establishes the evaluation model of error square sum and curve similarity to evaluate the denoising performance. Experiments show that the polynomial fitting has the greatest denoising error and slow convergence speed. The five-point cubic smoothing has the smallest overall denoising error, the median filtering algorithm can change the effect of smoothing effect by adjust it's moothing window, but ignores the authenticity of data. Therefore, the above three data denoising algorithms do not meet the requirements of telemetry data processing. In this thesis, an improved threshold function is proposed which effectively improves the data jump and excessive smoothing and reduce the denoising accuracy compared with the traditional thresholding function in order to makes the measured value closer to the true value. The algorithm is applied to the noise processing of four kinds of telemetry data, the results show that the denoising accuracy is improved significantly compared with the other three algorithms, which makes the measured value closer to the true value to reflect the changing trend of the original measurement data more truthfully, and the curve similarity is improved significantly, which are all above 80%.

Research on Construction Method of Wavelet Telemetry Data with Improved Threshold

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In order to strengthen the applicability of data denoising algorithm, this thesis study the common telemetry data denoising algorithm based on the data of...

Research on Construction Method of Wavelet Telemetry Data with Improved Threshold

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 76 - 84

DOI: https://doi.org/10.2478/ijanmc-2022-0018

Schlüsselwörter
Function Construction, Measurement Error, Telemetry Data, Curve Similarity

© 2022 Yangyang Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research on Construction Method of Wavelet Telemetry Data with Improved Threshold

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 76 - 84

DOI: https://doi.org/10.2478/ijanmc-2022-0018

SchlüsselwörterFunction Construction, Measurement Error, Telemetry Data, Curve Similarity

© 2022 Yangyang Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Function Construction, Measurement Error, Telemetry Data, Curve Similarity