Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(108.45KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Target Detection by Fuzzy Gustafson-Kessel Algorithm
Mousumi Gupta
Pages - 203 - 208     |    Revised - 05-04-2013     |    Published - 30-04-2013
Volume - 7   Issue - 2    |    Publication Date - April 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Target Detection, Clutter Rejection, Data Normalization, Fuzzy Clustering, Fuzzy Gustafson-Kessel (FGK).
ABSTRACT
Many commercially available radar systems offer a range of filter options but the problem of clutter rejection for target detection is still present in a number of situations. Rejection of clutter and detection of targets from radar captured data is a challenging task. Raw data captured by radar are not always scaled. A normalization technique has been proposed which transforms the radar captured data into 8 bit. As 8 bit data is easy to analyze and visualize. A modification on Fuzzy c-means has been done by developing Fuzzy Gustafson–Kessel (FGK) algorithm and the result shows robustness of this proposed method.
CITED BY (2)  
1 Simhachalam, B., & Ganesan, G. (2015). Performance comparison of fuzzy and non-fuzzy classification methods. Egyptian Informatics Journal.
2 Barrah, H., & Cherkaoui, A. (2014, April). A stabilizer mahalanobis distance applied to ellipses extraction using the fuzzy clustering. In Multimedia Computing and Systems (ICMCS), 2014 International Conference on (pp. 1059-1064). IEEE.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 D. Colak, R. J. Burkholder, and E. H. Newman, Multiple sweep method of moments analysis of electromagnetic scattering from 3D objects on ocean-like rough surfaces, Microwave and Optical Technology Letters, vol. 49, pp. 241-247, 2007.
2 L. Guo and K. Cheyoung, Light scattering models for a sphereical particle above a slightly delectric rough surface, Microwave and Optical Technology Letters, vol. 33, pp. 142-146, April 2002.
3 Steven P Jacobs, Joseph A. O'Sullivan, Automatic target recognition using sequences of high resolution radar range profiles, IEEE transactions on aerospace and electronic systems, Vol 36,No 2, 2000.
4 Rajesh K, Radar target detection in Weibull clutter by adaptive filtering with embedded CFAR,IEEE Electronics Letters, Vol 35 , 597-599,1999.
5 M.Gupta et al. Pattern Recognition Letters, Vol 33, pp.1682–1688, 2012.
6 Ilteris Demirkiran,Donald D.Weiner and Andrew Drozd , Effect of In-band Intermodulation Interference on Direct-Sequence Spread Spectrum (DSSS) Communication Systems for Electromagnetically Diverse Applications, 2007 IEEE.
7 Gang Wang , Jinxing Hao , Jian Ma, Lihua Huang , A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering, Expert Systems with Applications, 2010.
8 Dzung L. Pham, Spatial Models for Fuzzy Clustering, Computer Vision and Image Understanding, Vol 84,pp. 285–297 , 2001.
9 Tara.Saikumar, B.K.Anoop, P.S.Murthy, “Tara Kernel Fuzzy Clustering (TKFCM) for a Robust Adaptive Threshold Algorithm based on Level Set Method”, International Journal of Information Technology Convergence and Services, Vol.2, No.1, February 2012.
10 Ashish Ghosh , Niladri Shekhar Mishra , Susmita Ghosh, Fuzzy clustering algorithms for unsupervised change detection in remote sensing images, Information Sciences,2010.
11 Guangzhi Cao et al , The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals, IEEE Trans. on Image Processing,Vol 20, pp.625-640, 2011.
12 Leonardo R et al.,Fast Signal Analysis and Decomposition on Graphs using the Sparse Matrix Transform, in the Proceedings of the International Conference on Acoustic, Speech, and Signal Processing , March 14-19, 2010.
13 Gustafson, E. E. and Kessel , W. C., Fuzzy clustering with a fuzzy covariance matrix Proc. of the IEEE Conference on Decision and Control, San Diego, pp. 761–766. IEEE Press,Piscataway, NJ.1979.
Mr. Mousumi Gupta
Siikim Manipal Inst of Tech - India
mousmi_gt@yahoo.co.in