Signal sorting based on SVC & K-means clustering in ESM systems

Authors:
Qiang Guo;Wanhai Chen;Xingzhou Zhang;Zheng Li;Di Guan
Affiliations:
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China;College of Information and Communication Engineering, Harbin Engineering University, Harbin, China;College of Information and Communication Engineering, Harbin Engineering University, Harbin, China;Southwest Research Institute of Electronic Equipment, Chengdu, China;Harbin Normal University, Harbin, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining: Concepts, Models, Methods and Algorithms

Data Mining: Concepts, Models, Methods and Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Support vector clustering

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

As radar signal environments become denser and radar signals become more complex, the task of an ESM operator becomes more difficult. This paper presented a de-interleaving/recognition system of radar pulses based on the combination of SVC and K-means clustering. Compared the conventional de-interleaving system, it can produce more complex and compact clustering boundaries according to the distribution characteristics of data set and has good generalization performance. The simulation experiment result shows that the system can sort efficiently radar signals in the high density and complex pulses environment.