The problem of matching suitability for geomagnetic aided navigation is investigated from the viewpoint of pattern recognition in this article. In order to improve the classification accuracy of candidate matching areas, an intelligent classification method based on genetic algorithm and support vector machine is proposed. Firstly, the geomagnetic datasets and the factors influencing the classification performance of support vector machine are studied. Then support vector machine is employed as the classifier, and genetic algorithm is utilized for feature selection and support vector machine parameters optimization to improve the classification performance. Afterwards the multi-class support vector machine classifiers based on the one-against-one strategy are constructed for analyzing matching suitability. Experimental results show that the proposed method can greatly improve the classification accuracy of candidate matching areas, and moreover, the conclusions of this article can provide beneficial guidance for geomagnetic matching and route planning.
ZhouJLiuYXGeZL. Geomagnetic matching algorithm based on the probabilistic neural network. Proc IMechE, Part G: J Aerospace Engineering2011; 225(1): 120–126.
2.
Lin Y. Hausdorff-based RC and IESIL combined positioning algorithm for underwater geomagnetic navigation. EURASIP J Adv Signal Process 2010: 593238.
3.
Wang P, Wu YX, Hu XP, et al. Geomagnetic aided navigation suitability evaluation based on principal component analysis. In: Proceedings of the international conference on industrial control and electronics engineering, Xi’an, China, 2012, pp.324–329.
4.
LiuYXZhouJGeZL. A projecting pursuit based selection method for matching region in geomagnetism navigation. J Astronaut2010; 31(12): 2678–2682.
5.
Ma XJ, Liu HW, Xiao D, et al. Key technologies of geomagnetic aided inertial navigation system. In: Proceedings of the IEEE intelligent vehicles symposium, Xi'an, China, 2009, pp.464–469.
6.
ZhouXGLiSXYangJL. Selective criteria of characteristic area on geomagnetic map. J Chin Inertial Technol2008; 16(6): 694–698.
7.
LvYXWuMPHuXP. Support vector machine for geomagnetism aided navigation matching area selection. Ordnance Indus Automat2011; 30(1): 49–52.
8.
WangZWangSCZhangJS. A method of selection matching suitability characteristic parameter based on rough set theory in geomagnetism matching guidance. J Astronaut2009; 30(3): 1057–1063.
9.
Wang KD, Li Y and Rizos C. Research on terrain suitability of iterative closest contour point algorithm for underwater navigation. In: Proceedings of the 22nd international technical meeting of the satellite division of the institute of navigation, Georgia, USA, 2009, pp.866–870.
10.
ShenLCBuYLXuX. Research on matching area suitability for scene matching aided navigation. Acta Aeronaut Astronaut Sin2010; 31(3): 553–563.
11.
LiJYangXYangL. Mumford-Shah model based critical subset segmentation from navigation reference images. Acta Automat Sin2004; 30(1): 45–56.
12.
Xiao YJ, Zhou CP and Ding MY. Matching suitability analysis of reference map for scene – matching -based navigation. In: Proceedings of the international symposium on multispectral image processing, Wuhan, China, 1998, pp.100–103.
13.
Zhang GZ, Shen LC and Chang WS. Acquisition probability estimation model of density correlation scene matching system. In: Proceedings of the 2nd international conference on image and graphics, Hefei, China, 2002, pp.546–551.
14.
ZhaoJHWangSPWangAX. Study on the selection of the geomagnetic adaptable matching area based on the geomagnetic co-occurrence matrix. Geomat Inform Sci Wuhan Univ2011; 36(4): 446–449.
15.
VapnikVN. Statistical learning theory, New York: Wiley-Interscience, 1998.
16.
CoelhoCDasSChattopadhyayA. A hierarchical classification scheme for computationally efficient damage classification. Proc IMechE, Part G: J Aerospace Engineering2009; 223(G5): 497–505.
17.
HuangJTMaLHQianJX. Improved support vector machine for multi-class classification problems. J Zhejiang Univ (Eng Sci)2004; 38(12): 1633–1659.
18.
YuanSFChuFL. Fault diagnostics based on particle swarm optimisation and support vector machines. Mech Syst Signal Process2007; 21(4): 1787–1798.
19.
YakoubBFaridM. Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sensing2006; 44(11): 3374–3385.
20.
HuangHLChangFL. ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data. BioSystems2007; 90(2): 516–528.
21.
Chang CC and Lin CJ. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011; 2(3): 27.1–27.27.
22.
HollandJH. Adaptation in natural and artificial systems, Ann Arbor, Michigan: University of Michigan Press, 1975.
23.
SimeoniFCasalinoL. Evolutionary optimization of interplanetary trajectories: improvements from initial diversification. Proc IMechE, Part G: J Aerospace Engineering2011; 225(11): 1277–1288.
24.
SabetiMBoostaniRKatebiSD. Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed Signal Process Control2007; 2(2): 122–134.
JolliffeIT. Principal component analysis, 2nd edn. New York: Springer-Verlag, 2002.
27.
LeiYGHeZJZiYY. Application of an intelligent classification method to mechanical fault diagnosis. Exp Syst Appl2009; 36(6): 9941–9948.
28.
ZhuZOngY-SDashM. Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cyber B: Cybernetics2007; 37(1): 70–76.
29.
Liu Y, Wu MP, Hu XP, et al. Geomagnetism aided inertial navigation system. In: Proceedings of the 2nd international symposium on systems and control in aerospace and astronautics, Shenzhen, China, 2008, pp.2707–2711.
