- Title
- Deep learning-based approach for detecting trajectory modifications of cassini-huygens spacecraft
- Creator
- Aldabbas, Ashraf; Gal, Zoltan; Ghori, Khawaja; Imran, Muhammad; Shoaib, Muhammad
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185348
- Identifier
- vital:16649
- Identifier
-
https://doi.org/10.1109/ACCESS.2021.3064753
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 9, no. (2021), p. 39111-39125
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; Big data; Cassini-Huygens interplanetary project; Complex event; Knowledge representation; Neural network; Pattern processing; Sensory data
- Full Text
- Reviewed
- Funder
- This work was supported in part by the University of Debrecen, Hungary, under Project FIKP-20428-3/2018/FEKUTSTRAT, in part by the QoS-HPC-IoT Laboratory, and in part by the Deanship of Scientific Research at King Saud University under Project RG-1435-051.
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