Intra-pulse modulation recognition of unknown radar emitter signals using support vector clustering

Authors:
Gexiang Zhang;Haina Rong;Weidong Jin
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

Citing 11
Cited 0

Support vector clustering

The Journal of Machine Learning Research
Self-organizing feature maps for solving location-allocation problems with rectilinear distances

Computers and Operations Research
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient clustering scheme using support vector methods

Pattern Recognition
Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation

Expert Systems with Applications: An International Journal
Discretization of continuous attributes in rough set theory and its application

CIS'04 Proceedings of the First international conference on Computational and Information Science
Intra-pulse modulation recognition of advanced radar emitter signals using intelligent recognition method

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Support vector machines with huffman tree architecture for multiclass classification

CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A hybrid classifier based on rough set theory and support vector machines

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Radar emitter signal recognition based on feature selection algorithm

AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Global exponential stability of competitive neural networks with different time scales

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Unknown radar emitter signal (RES) recognition is an important issue in modern electronic warfare because the enemy's RESs are usually uncertain in the battlefield. Although unsupervised classifiers are used generally in many domains, few literatures deal with applications of unsupervised classifiers to RES recognition. In this paper, three unsupervised classifiers including competitive learning neural network (CLNN), self-organizing feature map neural network (SOMNN) and support vector clustering (SVC) are used to recognize unknown RESs. 135 RESs with 7 intra-pulse modulations are used to test the performances of the three classifiers. Experimental results show that SVC is only slightly superior to CLNN and is greatly inferior to SOMNN.