Target Attribute Identification Based on Multi-Class SVM and D-S Evidence Theory

Author

Jin, Xu ; Lan Jiangqiao ; Xu Jin

Author_Institution

Dept. of Early Warning Surveillance Intell., AFRA, Wuhan, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Concerning for air target attribute identification, an attribute identification method based on the combination of multi-class SVM and D-S evidence theory is proposed. The method constructs several multi-class support vector machine (SVM) classifiers, and generates the basic probability assignment (BPA) by the class-wise probability. Then D-S evidence theory is adopted to make the fusion and decision. Simulation results indicate that the method have a good identification of air target attribute, and prove the rationality and validity.

Keywords

pattern classification; probability; sensor fusion; support vector machines; target tracking; DS evidence theory; air target attribute identification; basic probability assignment; class wise probability; multiclass SVM classifier; support vector machine; target attribute identification; Fusion power generation; Fuzzy reasoning; Machine intelligence; Risk management; Statistical learning; Support vector machine classification; Support vector machines; Surveillance; Target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5364400

Filename

5364400