In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP)
classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target
recognition by using GP classification with automatic relevance determination (ARD) function. Compared with
k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic
model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that
our algorithm is self-tuning and has better recognition accuracy as well.
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