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 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 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 Guide Star Selection for the Three-FOV Daytime Star Sensor

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1457 ◽  
Author(s):  
Liang Wu ◽  
Qian Xu ◽  
Haojing Wang ◽  
Hongwu Lyu ◽  
Kaipeng Li
Keyword(s):  
Short Wave ◽  
Optimal Number ◽  
Reference Points ◽  
Filter Method ◽  
Star Sensor ◽  
Star Catalog ◽  
Swir Band ◽  
Star Identification ◽  
Guide Star ◽  
Star Catalogs

To realize the application of the star sensor in the all-day carrier platform, a three-field-of-view (three-FOV) star sensor in short-wave infrared (SWIR) band is considered. This new prototype employs new techniques that can improve the detection capability of the star sensor, when the huge size of star identification feature database becomes a big obstacle. Hence, a way to thin the guide star catalog for three-FOV daytime star sensor is studied. Firstly, an introduction of three-FOV star sensor and an example of three-FOV daytime star sensor with narrow FOV are presented. According to this model and the requirement of triangular star identification method, two constraints based on the number and the brightness of the stars in FOV are put forward for guide star selection. Then on the basis of these constraints, the improved spherical spiral method (ISSM) is proposed and the optimal number of reference points of ISSM is discussed. Finally, to demonstrate the performance of the ISSM, guide star catalogs are generated by ISSM, magnitude filter method (MFM), 1st order self-organizing guide star selection method (1st-SOPM) and the spherical spiral method (SSM), respectively. The results show that the guide star catalog generated by ISSM has the smallest size and the number and brightness characteristics of its guide stars are better than the other methods. ISSM is effective for the guide star selection in the three-FOV daytime star sensor.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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