A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

Guo Dong Zhang; Zhong Liu

doi:10.4028/www.scientific.net/AMR.734-737.3071

Paper Titles

A Gesture Recognition Algorithm Based on PCA and BP Neural Network
p.3053

A New Piecewise Rational Reparameterization Method
p.3057

A Novel Algorithm for Classifying Measuring Curves Based on Interval Estimation
p.3061

A Procedure to Evaluate the Indoor Global Quality by a Sub Objective-Objective Procedure
p.3065

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM
p.3071

Algorithm for Imaging Sonar Target Detection based on Geometry Characteristic and SVM
p.3075

An Anhydrous Bridge Recognition Algorithm in Aerial Image
p.3079

An Automatic Cloud Service Platform for Learning from Large-Scale Data
p.3085

Application of the Technology of Multi-Source Information Fusion in Industrial Monitoring and Fault Diagnosis
p.3089

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 734-737A Target Selection Algorithm of Seeker Based on...

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

Abstract:

Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy, and the simulation experiment in this paper confirms the correctness and effectiveness of this method.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 734-737)

Pages:

3071-3074

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.734-737.3071

Citation:

Cite this paper

Online since:

August 2013

Authors:

Guo Dong Zhang, Zhong Liu

Keywords:

Distribution Law, Prior Information, Support Vector Machine (SVM), Target Selection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] SUN Qing, ZHANG Qi-liang, SUN Yong. Evaluation to the Performance of MTI Radar Anti-passive Jamming. ICWMMN 2010, Beijing. (2010).

DOI: 10.1049/cp.2010.0671

Google Scholar

[2] Ellonen llkka, Kaarna Arto. Chaff Clutter Filtering From Radar Data with Discrete Wavelet Transform. IEEE Radar Conference, Rome. (2008).

DOI: 10.1109/radar.2008.4720756

Google Scholar

[3] DOU Zhen-zhong, Wang Li-sen. Research of Signal Processing System for Anti-jamming Based on Multifrequency Spread Spectrum and Hopping Spectrum. ICSP, Yangzhou. (1996).

DOI: 10.1109/icsigp.1996.571254

Google Scholar

[4] HU Bi-Ru, Wu Wen-jia, Dai Meng-yan, et al. Study on Property of Infrared Obscur of Artificial Fog. Journal of Infrared and Millimeter Waves. Vol. 25 (2006), p.131.

Google Scholar

[5] NIE Yong-fang，YAO Yi，WANG Chun-jian. Research on the Combat using of Chaff Centroid Jamming for Ship. Ship Electronic Engineering. Vol. 12 (2011), p.35.

Google Scholar

[6] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer Verlag. (1995).

Google Scholar

[7] Vapnik V N. Statistical learning theory. New York: Wiley. (1998).

Google Scholar

[8] G P Liu. Nonlinear Identification and Control: a Neural Network Approach. London, New York: Springer. (2001).

Google Scholar

[9] Gao J. The Theory of Neural Network and Its Simulation(second edition). Beijing: China Machine Press. (2010).

Google Scholar

[10] Rong H N, Zhang G X, Jin W D. Selection of Kernel Functions and Parameters for Support Vector Machines in System Identification. Journal of System Simulation. Vol. 18 (2006), p.3204.

Google Scholar