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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
Ruiming Liu, "Detection of Infrared Point Targets with Linear Eigentargets and Nonlinear Eigentargets", CSIE, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 338-343, doi:10.1109/CSIE.2009.378
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