论文标题
随机RX进行目标检测
Randomized RX for target detection
论文作者
论文摘要
这项工作通过众所周知的全球RX方法解决了目标检测问题。 RX方法将杂物作为多元高斯分布建模，并已使用内核方法扩展到非线性分布。虽然内核RX可以应对复杂的剪断器，但随着混乱像素的数量变大，它需要大量的计算资源。在这里，我们提出了随机傅立叶特征，以近似内核RX中的高斯内核，因此我们的发展保持了非线性的准确性，同时降低了现在由超参数控制的计算成本。合成和现实世界图像检测问题的结果都显示了所提出方法的空间和时间效率，同时提供了高检测性能。
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.
