Title :
Multi-kernel SVM based star pattern recognition for Celestial Navigation
Author :
Liu, Taiyang ; Wang, Shicheng ; Liu, Zhiguo ; Min, Haibo ; Li, Renbing
Author_Institution :
Dept. of Technol., High-Tech Inst. of HongQing, Xi´´an, China
Abstract :
This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition´s character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel function generation are cited in detail, and a star pattern recognition routine including an “Indexing + recognition” scheme, feature vector definition and generation, SVM training realization are designed and realized.
Keywords :
astronomical image processing; genetic algorithms; image matching; image recognition; indexing; learning (artificial intelligence); navigation; support vector machines; SVM training realization; celestial navigation; feature vector definition; genetic programming; indexing scheme; intelligent learning algorithm; multikernel SVM algorithm; multikernel function generation; star pattern recognition character; support vector machine; Accuracy; Algorithm design and analysis; Kernel; Noise; Pattern recognition; Support vector machines; Training;
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
DOI :
10.1109/COGINF.2010.5599814
