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中文摘要：
在主成分特征提取基础上，提出了一种把子空间二次综合判别函数（Subspace Quadratic Dynthetic Discriminant Function，SSQSDF）作为相关滤波器的红外目标检测算法．该算法把混合概率核主成分分析推广到混合概率模型，在核空间对样本进行特征提取，获取目标样本的低维主特征向量．对训练和待检测样本向主特征向量投影获得它们的低维特征分量，并把获取的特征量作为SSQSDF的样本参量．最后，SSQSDF滤波器输出大于给定阂值所对应的检测区域，将其作为检测目标．实验证明，该算法能较强抑制目标背景噪音，提高目标检测准确度，具有一定的可行性和有效性．
英文摘要：
Based on the feature extraction of principal component,a novel infrared target detection algorithm was proposed which using subspace quadratic synthetic discriminant function （SSQSDF）. Firstly, the kernel principal component analysis was extended to mixture probabilistic model, and the latter get the principal component vectors of target samples. Then, training samples and samples to be detected were projected on principal component vectors obtained previously to acquire their low-dimension feature components,and the obtained components are used as the sample parameters for the SSQSDF. The detected samples which had a higher SSQSDF filtering output than given threshold were considered as the detected targets. The proposed algorithm can evidently restrain clutter noise, improve target detection precision. Experimental results under complex scenery demonstrate that the proposed algorithm is feasibility and effectiveness.
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