Randomized RX for target detection
This work provides a more computationally efficient method for target detection in complex clutter environments, benefiting applications where real-time processing or resource constraints are critical.
This paper addresses the computational cost of kernel RX methods for target detection by using random Fourier features to approximate the Gaussian kernel. This approach maintains detection performance while significantly reducing computational resources, controlled by a hyperparameter.
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.