scholarly journals MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3027
Author(s):  
Lijian Sun ◽  
Yun Zhou
Keyword(s):  
Focal Length ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Distance Information ◽  
Noise Interference ◽  
Identification Time ◽  
Star Identification ◽  
Influence Of Noise

Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed star map is often difficult to use, because they might contain errors or be lost in some worst cases. To solve this problem, we proposed a multi-view double-triangle algorithm for star identification in this paper. This algorithm constructs double-triangle features of stars with the angle and distance information of star points. Moreover, to reduce the influence of noise interference on the identification accuracy of the model, we built multi-view double-triangle features for the observed star map to improve the robustness of the algorithm. Synthetic and real experiments show that our algorithm has a high identification accuracy of more than 98.4% in face of “false star” noises and “missing star” noises, and our algorithm is not affected by the focal length and the shooting angle of the star sensor. Moreover, the results also show that our algorithm has good robustness, short identification time and reduced storage costs, which could be beneficial in practice.

scholarly journals Efficient Star Identification Using a Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3684
Author(s):  
David Rijlaarsdam ◽  
Hamza Yous ◽  
Jonathan Byrne ◽  
Davide Oddenino ◽  
Gianluca Furano ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Determination ◽  
Lightweight Design ◽  
A Priori ◽  
Search Time ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Feature Extraction Method ◽  
Star Identification ◽  
Space Star

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.

scholarly journals Non-Dimensional Star-Identification

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2697
Author(s):  
Carl Leake ◽  
David Arnas ◽  
Daniele Mortari
Keyword(s):  
Optical Axis ◽  
Focal Length ◽  
Field Of View ◽  
Wide Field ◽  
Identification Algorithm ◽  
Algorithm Performance ◽  
Star Identification ◽  
Wide Field Of View ◽  
Axis Offset ◽  
Accuracy Speed

This study introduces a new “Non-Dimensional” star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit. The algorithm performance is compared in terms of accuracy, speed, and robustness to the Pyramid algorithm. These comparisons highlight the clear advantages that a combined approach of these methodologies provides.

scholarly journals Lost-in-Space Star Identification Using Planar Triangle Principal Component Analysis Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Fuqiang Zhou ◽  
Tao Ye
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Star Catalog ◽  
Star Identification ◽  
Triangle Method ◽  
Space Star ◽  
Guide Star

It is a challenging task for a star sensor to implement star identification and determine the attitude of a spacecraft in the lost-in-space mode. Several algorithms based on triangle method are proposed for star identification in this mode. However, these methods hold great time consumption and large guide star catalog memory size. The star identification performance of these methods requires improvements. To address these problems, a star identification algorithm using planar triangle principal component analysis is presented here. A star pattern is generated based on the planar triangle created by stars within the field of view of a star sensor and the projection of the triangle. Since a projection can determine an index for a unique triangle in the catalog, the adoption of thek-vector range search technique makes this algorithm very fast. In addition, a sharing star validation method is constructed to verify the identification results. Simulation results show that the proposed algorithm is more robust than the planar triangle andP-vector algorithms under the same conditions.

scholarly journals A Survey of Lost-in-Space Star Identification Algorithms Since 2009

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2579 ◽  
Author(s):  
David Rijlaarsdam ◽  
Hamza Yous ◽  
Jonathan Byrne ◽  
Davide Oddenino ◽  
Gianluca Furano ◽  
...  
Keyword(s):  
Extraction Method ◽  
A Priori ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Critical Component ◽  
Relative Performance Evaluation ◽  
Feature Extraction Method ◽  
Star Identification ◽  
Qualitative Representation ◽  
Space Star

The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.

scholarly journals An Efficient and Robust Star Identification Algorithm Based on Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7686
Author(s):  
Bendong Wang ◽  
Hao Wang ◽  
Zhonghe Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Rotation Invariant ◽  
One Dimensional ◽  
Identification Rate ◽  
Star Identification ◽  
Space Star ◽  
Fully Connected

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.

STAR IDENTIFICATION ALGORITHM FOR UNCALIBRATED, WIDE FOV CAMERAS

2015 ◽  
Vol 149 (6) ◽  
pp. 182 ◽  
Author(s):  
Mohamad Javad Ajdadi ◽  
Mahdi Ghafarzadeh ◽  
Mojtaba Taheri ◽  
Ehsan Mosadeq ◽  
Mahdi Khakian Ghomi
Keyword(s):  
Identification Algorithm ◽  
Star Identification ◽  
Wide Fov

scholarly journals An End-to-End Identification Algorithm for Smearing Star Image

2021 ◽  
Vol 13 (22) ◽  
pp. 4541
Author(s):  
Jinliang Han ◽  
Xiubin Yang ◽  
Tingting Xu ◽  
Zongqiang Fu ◽  
Lin Chang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Semantic Information ◽  
The Self ◽  
Identification Algorithm ◽  
Identification Rate ◽  
Star Image ◽  
Star Identification ◽  
Angular Velocities ◽  
End To End

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

scholarly journals Research on Dynamic Identification of Servo Motor Load Inertia Based on the Error Gain Factor Model

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6664
Author(s):  
Fang Xie ◽  
Fei Yu ◽  
Chaochen An
Keyword(s):  
Factor Model ◽  
Slow Motion ◽  
Gain Factor ◽  
Moment Of Inertia ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Servo Motor ◽  
Dynamic Identification ◽  
Identification Method ◽  
The Moment

Aiming at solving the problems of slow motion and positioning deviation caused by the change of the moment of inertia of the servo motor due to different loads, an identification method for the moment of inertia on the basis of the error gain factor model is introduced into the controller, so that the moment of inertia can be obtained accurately and quickly under dynamic conditions. First, the electromagnetic and motion equation of the permanent magnet synchronous motor is built, and the logical relationship between the moment of inertia, torque, speed and other physical quantities is derived, so that the moment of inertia can be dynamically acquired. Second, in order to increase the identification accuracy, an adaptive function is introduced in the inertia identification model to replace the fixed parameters as an error gain factor (EGF). Third, the accuracy parameter is defined, and the identification algorithm on the basis of the EGF model is compared with the accuracy parameters of the existing identification method, which verifies that the improved algorithm has a better accuracy and speed. Finally, on the experimental platform, the working condition of unsteady speed is simulated. It is further verified that the proposed method has a high anti-interference capability.

A New Star Identification Algorithm based on Improved Hausdorff Distance for Star Sensors

2013 ◽  
Vol 49 (3) ◽  
pp. 2101-2109 ◽  
Author(s):  
Quan Wei ◽  
Xu Liang ◽  
Fang Jiancheng
Keyword(s):  
Hausdorff Distance ◽  
Identification Algorithm ◽  
Star Identification

Optical system design of star sensor with long focal length and athermalization

2020 ◽  
Vol 49 (9) ◽  
pp. 20200061-20200061
Author(s):  
伍雁雄 Yanxiong Wu ◽  
乔健 Jian Qiao ◽  
王丽萍 Liping Wang
Keyword(s):  
System Design ◽  
Optical System ◽  
Focal Length ◽  
Star Sensor ◽  
Optical System Design
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