Randomized RX for target detection

TL;DR

This paper introduces a computationally efficient variant of the kernel RX target detection method using random Fourier features, maintaining high accuracy while reducing resource demands in complex clutter scenarios.

Contribution

It proposes a novel approximation of the kernel RX method with random Fourier features, significantly improving efficiency without sacrificing detection performance.

Findings

Achieves high detection accuracy on synthetic and real-world data.

Reduces computational cost compared to traditional kernel RX.

Maintains nonlinear modeling capabilities with fewer resources.

Abstract

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.