An Efficient and Robust Star Identification Algorithm Based on Neural Networks
| Autor: | Hao Wang, Zhonghe Jin, Ben-dong Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Chemical technology TP1-1185 Star (graph theory) Overfitting one-dimensional Convolutional NeuralNetwork Biochemistry Convolutional neural network Atomic and Molecular Physics and Optics Article Analytical Chemistry modified log-polar mapping Identification (information) Noise Stars star identification Robustness (computer science) Neural Networks Computer Electrical and Electronic Engineering Instrumentation Algorithm Algorithms |
| Zdroj: | Sensors, Vol 21, Iss 7686, p 7686 (2021) Sensors Volume 21 Issue 22 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly. In this paper, a modified log-Polar mapping is used to constructed rotation-invariant star patterns. Then a 1D CNN is utilized to classify the star patterns associated with guide stars. In the 1D CNN model, a global average pooling layer is used to replace fully-connected layers to reduce the number of parameters and the risk of overfitting. Experiments show that the proposed algorithm is highly robust to position noise, magnitude noise, and false stars. The identification accuracy is 98.1% with 5 pixels position noise, 97.4% with 5 false stars, and 97.7% with 0.5 Mv magnitude noise, respectively, which is significantly higher than the identification rate of the pyramid, optimized grid and modified log-polar algorithms. Moreover, the proposed algorithm guarantees a reliable star identification under dynamic conditions. The identification accuracy is 82.1% with angular velocity of 10 degrees per second. Furthermore, its identification time is as short as 32.7 miliseconds and the memory required is about 1920 kilobytes. The algorithm proposed is suitable for current embedded systems. |
| Databáze: | OpenAIRE |
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