Rayleigh quotient quadratic correlation filter (RQQCF) is an important technique for target detection. Since it operates directly on image data, satisfying results can’t be always achieved when it is used in infrared target detection. Higher-order statistical properties of the image can effectively suppress the noise and clutter and improve the detection results which can be realized by means of kernel method in kernel space. In this paper, kernel Rayleigh quotient quadratic correlation filter (KRQQCF) was developed by extending RQQCF to the higher-dimensional space, i.e., the kernel space. Though the derivation was completed, this kernel filter couldn’t be achieved directly. Kernel feature extraction method was proposed to settle this problem. The algorithm was used to detect infrared targets, and kernel principal component analysis(KPCA) was adopted to obtain this KRQQCF in experiments. Experimental results using real-life infrared images confirm the excellent performance of KRQQCF, and that KRQQCF outperforms RQQCF significantly in infrared target detection. Consequently, KRQQCF is an effective method for infrared target detection and can achieve accurate detection results.