核空间与二次相关滤波器融合的红外目标检测

针对二次相关滤波器（QCF）与核空间特征相结合在红外目标检测中的应用，提出KSSQSDF核直接映射法与MPKPCA-SSQSDF核特征提取融合法。前者对低维空间下的QCF直接进行高维映射，使其转化为核空间下的非线性相关滤波器；后者采用核空间进行特征提取，对提取后的特征向量使用低维空间的相关滤波器，用于红外目标检测。通过实验分析2种算法间的相互联系，在目标检测结果及计算复杂性等方面的差异，结果表明，2种算法的检测精度大致相同，均明显优于低维空间的QCF检测，但MPKPCA—SSQSDr核特征提取融合法不受QCr种类限制，检测时间短，具有广泛性，在某种程度上可以代替KSSQSDF核直接映射法。

Infrared Target Detection of Kernel Space and Quadratic Correlation Filter Fusion

Aiming at the Quadratic Correlation Filter（QCF） associated with kernel space is applied to infrared target detection, this paper proposes KSSQSDF kernel direct mapping algorithm and MPKPCA-SSQSDF kernel feature extraction fusion algorithm. KSSQSDF directly extends QCF from low dimensional space to high dimensional space, thus QCF is transformed to nonlinear correlation filter in kernel space. MPKPCA-SSQSDF first extracts target feature under kernel space, and then the extracted feature vector is used to QCF of low dimensional space for infrared target detection. Through experiment, the difference of detection result and computational complexity are analytically given when KSSQSDF and MPKPCA-SSQSDF are used respectively. The result shows kernel direct mapping algorithm and kernel feature extraction fusion algorithm have the similar detection accuracy, which evidently exceed QCF of low dimensional space. But MPKPCA-SSQSDF kernel feature extraction fusion algorithm does not confine the type of QCF, and has shorter detection time. So it has more extensive application range, and to some extent it can substitute for KSSQSDF kernel direct mapping algorithm.