This work tackles the target detection problem through the well-known global RX method. The RX method models the clutter as a multivariate Gaussian distribution, and has been extended to nonlinear distributions using kernel methods. While the kernel RX can cope with complex clutters, it requires a considerable amount of computational resources as the number of clutter pixels gets larger. Here we propose random Fourier features to approximate the Gaussian kernel in kernel RX and consequently our development keep the accuracy of the nonlinearity while reducing the computational cost which is now controlled by an hyperparameter. Results over both synthetic and real-world image target detection problems show space and time efficiency of the proposed method while providing high detection performance.

翻译：本研究通过经典的全局RX方法解决目标检测问题。RX方法将杂波建模为多元高斯分布，并已通过核方法扩展至非线性分布。尽管核RX能够处理复杂杂波，但随着杂波像素数量增加，其需要大量计算资源。本文提出采用随机傅里叶特征来近似核RX中的高斯核，从而在保持非线性精度的同时降低计算成本，该成本现可通过超参数调控。在合成与真实图像目标检测问题上的实验结果表明，所提方法在提供高检测性能的同时，具备优越的空间与时间效率。