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

In the course of satellite observations using satellite laser ranging (SLR), a key task is pointing the telescope with high precision. Positioning the steering system’s mechanical parts with zero error is impossible. Accordingly, we must analyze and account for pointing errors by incorporating the telescope mounting errors themselves into the modeling error. Such models are far from trivial owing to the factors such as satellite azimuth, altitude, perhaps distance, or meteorological data.
In this article, we explain how the data for the telescope pointing inaccuracy model (TIM) was collected and how a neural network was used to build a very precise TIM for the Golisiiv 1824 SLR station in Kyiv.
We have focused our efforts on the suggested approach’s positive aspects based on our experience of using it to find practical solutions. Our practical recommendations may also be interesting for anyone working with hardware, especially in analyzing their errors. The key proof of the effectiveness of the approach is the serious increase in the number of satellites successfully tracked, especially for “blind” paths, when the satellite is not visible to the observer through the telescope guide.
</abstract>

In the course of satellite observations using satellite laser ranging (SLR), a key task is pointing the telescope with high precision. Positioning the steering system’s mechanical parts with zero error is impossible. Accordingly, we must analyze and account for pointing errors by incorporating the telescope mounting errors themselves into the modeling error. Such models are far from trivial owing to the factors such as satellite azimuth, altitude, perhaps distance, or meteorological data.
In this article, we explain how the data for the telescope pointing inaccuracy model (TIM) was collected and how a neural network was used to build a very precise TIM for the Golisiiv 1824 SLR station in Kyiv.
We have focused our efforts on the suggested approach’s positive aspects based on our experience of using it to find practical solutions. Our practical recommendations may also be interesting for anyone working with hardware, especially in analyzing their errors. The key proof of the effectiveness of the approach is the serious increase in the number of satellites successfully tracked, especially for “blind” paths, when the satellite is not visible to the observer through the telescope guide.

Neural Network Applied to Telescope Pointing Inaccuracy Model

Artificial Satellites

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

{"article-title":"Neural Network Applied to Telescope Pointing Inaccuracy Model"}

In the course of satellite observations using satellite laser ranging (SLR), a key task is pointing the telescope with high precision. Positioning the steering...

Layer type	Number of neurons	Number of parameters	Activation
InputLayer	4	0	None
Dense	64	320	tanh
Dense	64	4160	relu
Dense	64	4160	relu
Dense	64	4160	relu
Dense	32	2080	relu
Dense	32	1056	relu
Dense	32	1056	relu
Dense	32	1056	relu
Dense	16	528	relu
Dense	16	272	relu
Dense	16	272	relu
Dense	16	272	relu
Dense	8	136	relu
Dense	8	72	relu
Dense	8	72	relu
Dense	8	72	relu
Dense	2	18	sigmoid

Neural Network Applied to Telescope Pointing Inaccuracy Model

Published Online: Jul 06, 2024

Page range: 55 - 63

Received: Feb 07, 2024

Accepted: Jun 17, 2024

DOI: https://doi.org/10.2478/arsa-2024-0004

Keywords
telescope pointing model, neural network, satellite laser ranging

© 2024 Vitaliy Zhaborovskyy et al., published by Sciendo

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

Figure 1.

Figure 2.

Figure 3.

General structure of the TIM network, each row is a layer. Total number of trainable parameters is 19,762.

Neural Network Applied to Telescope Pointing Inaccuracy Model

Published Online: Jul 06, 2024

Page range: 55 - 63

Received: Feb 07, 2024

Accepted: Jun 17, 2024

DOI: https://doi.org/10.2478/arsa-2024-0004

Keywordstelescope pointing model, neural network, satellite laser ranging

© 2024 Vitaliy Zhaborovskyy et al., published by Sciendo

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

Figure 1.

Figure 2.

Figure 3.

General structure of the TIM network, each row is a layer. Total number of trainable parameters is 19,762.

Keywords
telescope pointing model, neural network, satellite laser ranging