The Community for Technology Leaders
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 338-343
The linear subspace algorithm and nonlinear subspace algorithm is explored to detect point targets. We call them as linear Eigentargets and nonlinear Eigentargets. Linear principal component analysis (LPCA) is based on the second-order correlations without taking higher-order statistics into account. So LPCA is only appropriate to represent the data with a Gaussian distribution. That results in the performance limitation of linear Eigentargets detection based on LPCA. For improving detection performance, we extend linear Eigentargets to its nonlinear version, nonlinear Eigentargets, in this paper. Because the nonlinear PCA is capable of capturing the part of higher-order statistics, the better detection performance can be achieved.
target detection, infrared point target, subspace, PCA

R. Liu, "Detection of Infrared Point Targets with Linear Eigentargets and Nonlinear Eigentargets," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 338-343.
90 ms
(Ver 3.3 (11022016))