Next Article in Journal
Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton
Next Article in Special Issue
Joint Unsupervised Learning of Depth, Pose, Ground Normal Vector and Ground Segmentation by a Monocular Camera Sensor
Previous Article in Journal
Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors
Previous Article in Special Issue
Measurement for the Thickness of Water Droplets/Film on a Curved Surface with Digital Image Projection (DIP) Technique
Open AccessLetter

Efficient Star Identification Using a Neural Network

1
Intel Corporation, Intel R&D Ireland Ltd, Collinstown, Collinstown Industrial Park, Co., Kildare W23 CX68, Ireland
2
European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, the Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3684; https://doi.org/10.3390/s20133684
Received: 27 May 2020 / Revised: 23 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Camera as a Smart-Sensor (CaaSS))
The required precision for attitude determination in spacecraft is increasing, providing a need for more accurate attitude determination sensors. The star sensor or star tracker provides unmatched arc-second precision and with the rise of micro satellites these sensors are becoming smaller, faster and more efficient. The most critical component in the star sensor system is the lost-in-space star identification algorithm which identifies stars in a scene without a priori attitude information. In this paper, we present an efficient lost-in-space star identification algorithm using a neural network and a robust and novel feature extraction method. Since a neural network implicitly stores the patterns associated with a guide star, a database lookup is eliminated from the matching process. The search time is therefore not influenced by the number of patterns stored in the network, making it constant (O(1)). This search time is unrivalled by other star identification algorithms. The presented algorithm provides excellent performance in a simple and lightweight design, making neural networks the preferred choice for star identification algorithms. View Full-Text
Keywords: star identification; deep learning; lost-in-space; star feature extraction star identification; deep learning; lost-in-space; star feature extraction
Show Figures

Figure 1

MDPI and ACS Style

Rijlaarsdam, D.; Yous, H.; Byrne, J.; Oddenino, D.; Furano, G.; Moloney, D. Efficient Star Identification Using a Neural Network. Sensors 2020, 20, 3684.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop