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 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 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 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.

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.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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