Star Map Dividing Algorithm Based On Star Pattern Recognition

2006 ◽  
Author(s):  
Hu Haidong ◽  
Huang Xianlin ◽  
Wu Tianbao ◽  
Li Haijun
Keyword(s):  
Pattern Recognition ◽  
Star Pattern ◽  
Star Pattern Recognition

Computer-aided star pattern recognition with astrometry.net: in-flight support of telescope operations on SOFIA

2016 ◽  
Author(s):  
Karsten Schindler ◽  
Dustin Lang ◽  
Liz Moore ◽  
Martin Hümmer ◽  
Jürgen Wolf ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Star Pattern ◽  
Computer Aided ◽  
Star Pattern Recognition

Optimization of a Star Pattern Recognition Algorithm for Attitude Determination Using a Multi-Objective Genetic Algorithm

2005 ◽  
Author(s):  
Ebenezer Seisie-Amoasi ◽  
Brian G. Williams ◽  
Marco P. Schoen
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Attitude Determination ◽  
Recognition Algorithm ◽  
Control Parameters ◽  
Pattern Recognition Algorithm ◽  
Multi Objective ◽  
Star Pattern ◽  
Multi Objective Genetic Algorithm ◽  
Star Pattern Recognition

Attitude determination for unmanned spacecrafts usually employs star trackers. The specifications for these devices dictate fast, reliable, robust, and autonomous algorithms to satisfy various mission constraints. This results into simple algorithms for reduced power consumption and reduced overall weight. Optimizing a Star Pattern Recognition Algorithm (SPRA), using an imbedded star map, requires the optimization of the genetic operators that constitute the SPRA and the control parameters within the SPRA. Simultaneous optimization of the control parameters of the SPRA results into a multi-objective and multi-parameter constrained optimization problem. The optimizing of genetic algorithms is often time consuming and rather tedious by nature. In this work, a Multi-Objective Genetic Algorithm (MOGA) acting as a meta-level GA is applied together with a double objective transition selection scheme to achieve the optimization. This approach results in significantly expediting the cost assignment process. By evolving a pareto set, an optimization population element rule is determined to exist between the control parameters of the SPRA. The existence of this rule ensures effective balance between population exploitation and exploration in the algorithm estimation process. This leads to effective solutions for finding the optimum with multiple concurrent objectives while taking the constraints into consideration. Simulation results using the optimized parameters for the SPRA indicate an improvement of the recognition accuracy from less than 60% to 100% as well as a reduction of the processing time of over 2000 generations to under 250 generations at 99% precision.

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