  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.
