Air Targets Recognition Based on Feature Analysis and Particle Swarm Optimization K-Means Clustering

Shao Lei Wang; Wei Yi Chen

doi:10.4028/www.scientific.net/AMR.490-495.1718

Paper Titles

The Optimization Design of Vacuum Chamber in Vacuum Electron Beam Welding Machine
p.1699

Simulation Analysis of Production Control Methods in Manufacturing Systems
p.1704

An Improved Generalized Predictive Control for AUV Yaw
p.1709

Analysis of Directional Accuracy of Three Single Arrays
p.1714

Air Targets Recognition Based on Feature Analysis and Particle Swarm Optimization K-Means Clustering
p.1718

The Application of Double Closed-Loop Unit Gain Negative Feedback and BP Neural Network Controller in Single Inverted Pendulum
p.1723

The Design and Test for the Intermittent Work-Style Drying System
p.1728

A Trinocular Stereo-Matching Algorithm Based on Adaptive Support-Weight for Mobile Service Robot
p.1733

Stage Boom Machinery Control Based on Adaptive Fuzzy PID
p.1738

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495Air Targets Recognition Based on Feature Analysis...

Air Targets Recognition Based on Feature Analysis and Particle Swarm Optimization K-Means Clustering

Abstract:

The threat which the formation of ships will face is much more complex and various than ever before. To recognise targets right and fast is the precondition of air defence. On the basis of analysing the information from surveillance radar, the target recognition of fleet air defence is translated into optimal clustering problem, and the model for identifying air-targets is established. Through standardizing the attribute values of samples and then the Principal Components Analysis(PCA) and Particle Swarm Optimization(PSO) is introduced and the best solution is established. With the advantage of PSO's intelligent searching mechanism, global optimization, robustness and strong classification ability, the model can deal with such problems simply quickly and correctly，and help the commanders make decisions effectively.

You might also be interested in these eBooks

Mechatronics and Intelligent Materials II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

1718-1722

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.1718

Citation:

Cite this paper

Online since:

March 2012

Authors:

Shao Lei Wang, Wei Yi Chen

Keywords:

K Means Clustering, Particle Swarm Optimization Algorithm (PSO), Principal Component Analysis (PCA), Targets Identification

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Baoqing Yang, Haiping Liu, Yuncheng Li: Modern Defence Technology, Vol. 1(2003), pp.25-26, in Chinese.

Google Scholar

[2] Naihua Song, Qinghua Xing: Systems Engineering and Electronic Vol. 1(2006),P. 1279-1281, in Chinese.

Google Scholar

[3] Yushuang Wang, Yaozhu Pei, Shihui Wu, et al: Journal of Projectiles, Rockets, Missiles and Guidance Vol. 2(2006),P. 644-646, in Chinese.

Google Scholar

[4] Jianhai Chen, Jie Feng: China Radar, Vol. 2(2010),P. 4-6, in Chinese.

Google Scholar

[5] Shaoshun Chen, Yinlong Wang: Electronics Optics & Control, Vol. 1 (2004),P. 28-30, in Chinese.

Google Scholar

[6] Dongsheng Zou, Longhua She: Journal of Vibration Measurement & Diagnosis Vol. 1(2009),P. 96-100, in Chinese.

Google Scholar

[7] L. Kaufman, P. J. Rousseeuw: Finding groups in data: An introduction to cluster analysis(John Wiley & Sons, New York 1990).

Google Scholar

[8] J. Kennedy, R. Eberhart. In Proceedings of the 1995 IEEE international conference on neural network Vol. 4(1995),P. 1942–(1948).

Google Scholar

[9] Zhirong Hou, Zhensu Lu: Computer Simulation, Vol. 10(2003), P. 68-70, in Chinese.

Google Scholar

[10] E. Lumer, B. Faieta: Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animals, vol. 3(1994),P. 499–508.

Google Scholar