An Efficient and Robust Star Identification Algorithm Based on Neural Networks
Abstract
:1. Introduction
- A modified Log-Polar transform is used for star pattern construction. With the help of modified LPT, the training time of the network is reduced and the robustness of the network is improved.
- A 1D CNN is introduced for star pattern classification. The designed network could deal with position noise, magnitude noise, false stars and angular velocities.
- The global average pooling is introduced into the 1D CNN network to reduce the size of the network. Consequently, the designed network can be implemented on on-board processors.
2. Algorithm Description
2.1. Star Pattern Construction
2.2. Neural Network Architecture
2.3. Construction of the Training Dataset
- The magnitude of the guide star should be less than 6.0 Mv.
- Double stars or binary stars are labelled as a single star.
- Step1: Select a guide star as the reference star . Set the optic axis of the star tracker to point at S, which means the projection of lies at the center of the image. Then the neighboring stars appearing in the field of view are also projected from celestial coordinate system into the image coordinate system as shown in Figure 2a.
- Step2: The modified LPT transform is performed for every neighboring stars. A set of logarithmic distances and relative angles is obtained. Then same to the vector construction procedure, the log-polar coordinates of the neighboring stars are discreted and a vector is constructed, as shown in Figure 2b. is considered as the basic pattern of the reference star .
- Step3: Data augmentation is performed to reduce overfitting and enhance the generalization power of the network, which is also the major way to enhance the robustness of the algorithm. The details of data augmentation are described as follows:
- Random position deviations varying from pixels to 5 pixels were added to each star’s coordinates. As shown in Figure 2a, the curves with arrow denotes the direction of translation of each star.
- A random magnitude deviation varying from Mv to 0.5 Mv were added to each star. Therefore, some stars might appear or disappear due to the magnitude noise. As shown in Figure 2, and are the missing stars, and the corresponding elements of is set as 0.
- One to five false stars with random positions and magnitudes are added. As shown in Figure 2, and are the false stars added in the scene.
- In order to improve the rotation invariance of the algorithm, the basic pattern is shifted from to by step.
- Dynamic condition would decrease the magnitude limit of the star tracker and lead to missing stars. In order to make the network robust to dynamic condition, the magnitude limit is set from 4 Mv to 6 Mv, and the stars with a magnitude beyond the limit will be dropped.
2.4. Star Identification Algorithm
- Image Preprocessing. Star points are extracted via centroid extraction algorithm.
- Reference Star Determination. The nearest star to the center of the image is taken as the reference star S.
- Star Pattern Construction. The star pattern is generated by modified LPT.
- Star Pattern Classification. Input the star pattern to the proposed network, then the ID of the reference star and the corresponding probability are obtained.
- Validation. Unless the reference star is not classified as a false star and , this identification is considered as a success. Otherwise, the reference star may be a false star or the ID is wrong. Then a new reference star will be selected, which means to go back to step 2.
- Remaining Stars Identification. Identify the remaining stars in the image by the angular distances between them and the reference star.
3. Experiments and Results
3.1. Training of the Network
3.2. Comparison and Analysis
3.2.1. Robustness to Star Positional Noise
3.2.2. Robustness to False Stars
3.2.3. Robustness to Magnitude Noise
3.2.4. Robustness to Rotation Velocity of the Star Tracker
3.2.5. Performance of the Proposed Idea on Real Images
3.2.6. Time and Memory Performance
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameter | Value |
---|---|
Resolution of CMOS | |
Size of One Pixel | |
FOV | |
Highest Visual Magnitude | Mv |
Full Well Charge | 13,500 |
Temporal Noise | 13 |
Dark current signal | 125 |
Fixed Pattern Noise | <1 LSB |
Photon non-uniformity response noise | <1% RMS of signal |
Astronomical Background | 10 Mv |
Exposure time | 16 ms |
Algorithm | Identification Time | Memory Consumption |
---|---|---|
Proposed algorithm | 32.7 ms | 1920.6 KB |
Pyramid algorithm | 341.2 ms | 2282.3 KB |
Optimized Grid algorithm | 178.7 ms | 348.1 KB |
Modified LPT algorithm | 65.4 ms | 313.5 KB |
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Wang, B.; Wang, H.; Jin, Z. An Efficient and Robust Star Identification Algorithm Based on Neural Networks. Sensors 2021, 21, 7686. https://doi.org/10.3390/s21227686
Wang B, Wang H, Jin Z. An Efficient and Robust Star Identification Algorithm Based on Neural Networks. Sensors. 2021; 21(22):7686. https://doi.org/10.3390/s21227686
Chicago/Turabian StyleWang, Bendong, Hao Wang, and Zhonghe Jin. 2021. "An Efficient and Robust Star Identification Algorithm Based on Neural Networks" Sensors 21, no. 22: 7686. https://doi.org/10.3390/s21227686
APA StyleWang, B., Wang, H., & Jin, Z. (2021). An Efficient and Robust Star Identification Algorithm Based on Neural Networks. Sensors, 21(22), 7686. https://doi.org/10.3390/s21227686