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

<p>With the rapid development of space technology, the increasing number of spacecraft, in-orbit risk also increases, how to ensure that the spacecraft safety and reliability is particularly important. Prediction technology can predict the failure of the spacecraft in advance, and it has won valuable time for the fault of the spacecraft troubleshooting, thereby increasing the safety and reliability of spacecraft operation. In this paper, based on the non-stationary and periodicity of telemetry data. Based on the wavelet analysis, the prediction of the data is introduced, the establishment of a short-term forecasting model based on Mallat algorithm. The experimental results show that the prediction curve is basically consistent with the actual curve.</p>
</abstract>

With the rapid development of space technology, the increasing number of spacecraft, in-orbit risk also increases, how to ensure that the spacecraft safety and reliability is particularly important. Prediction technology can predict the failure of the spacecraft in advance, and it has won valuable time for the fault of the spacecraft troubleshooting, thereby increasing the safety and reliability of spacecraft operation. In this paper, based on the non-stationary and periodicity of telemetry data. Based on the wavelet analysis, the prediction of the data is introduced, the establishment of a short-term forecasting model based on Mallat algorithm. The experimental results show that the prediction curve is basically consistent with the actual curve.

Application of Wavelet Analysis in The Prediction of Telemetry Data

International Journal of Advanced Network, Monitoring and Controls

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

{"article-title":"Application of Wavelet Analysis in The Prediction of Telemetry Data"}

With the rapid development of space technology, the increasing number of spacecraft, in-orbit risk also increases, how to ensure that the spacecraft safety and...

Time	24	60	120	240
Mean Value	608.618	620.9572	622.6287	622.6287
Variance	198.2080	604.1601	611.2902	674.6010

Time	24	60	120	240
Mean Value	29.2479	35.413	37.1234	39.2378
Variance	10.3366	30.9019	37.4902	42.5010

Application of Wavelet Analysis in The Prediction of Telemetry Data

Pubblicato online: 08 ott 2019

Pagine: 28 - 34

DOI: https://doi.org/10.21307/ijanmc-2019-044

Parole chiave
Wavelet Analysis, Fourier Transform, Periodic Autoregression, Models, Mallat

© 2019 Xu Jiangtao et al., published by Sciendo

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

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

THE TEST RESULTS OF A REMOTE SENSING DATA 1 STATIONARITY

THE TEST RESULTS OF A REMOTE SENSING DATA 2 STATIONARITY

Application of Wavelet Analysis in The Prediction of Telemetry Data

Pubblicato online: 08 ott 2019

Pagine: 28 - 34

DOI: https://doi.org/10.21307/ijanmc-2019-044

Parole chiaveWavelet Analysis, Fourier Transform, Periodic Autoregression, Models, Mallat

© 2019 Xu Jiangtao et al., published by Sciendo

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

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

THE TEST RESULTS OF A REMOTE SENSING DATA 1 STATIONARITY

THE TEST RESULTS OF A REMOTE SENSING DATA 2 STATIONARITY

Parole chiave
Wavelet Analysis, Fourier Transform, Periodic Autoregression, Models, Mallat