中文摘要：

针对传统的基于滑窗自适应门限恒虚警检测方法在非高斯环境下和多目标干扰环境下，性能下降的问题，提出了一种基于拟合优度检验的恒虚警检测方案，该方法利用了目标回波与背景杂波统计特性的差异，通过检验待检单元的回波样本是否服从背景分布来检测目标：如果待检单元样本服从背景分布，则有理由相信待检单元回波源于背景杂波，从而判断没有目标存在；否则，将判断有目标存在。和传统的基于自适应门限的检测方法相比，该方法受背景分布特性和干扰目标的影响很小。仿真实验表明，在尖锐的非高斯杂波环境下以及多目标干扰环境下，都能保持更优的检测性能。

英文摘要：

This paper investigates a new constant false alarm rate （CFAR） detector based on goodness-of-fit （GOF） test. It uses the difference between the distribution characteristics of the background and the targets and decides whether a target is present by testing if the samples in the cell under test follow the distribution of the background. If the samples of the cell under test follow the distribution of the background, it＇ s justified to believe that the samples of the cell under test are merely originated from the background clutter and no target will be declared, otherwise, a target will be declared. The new detector is less affected by the underlying background and interfering targets. Simulation results show that it outperforms the conventional detector in heavy-tailed non-Gaussian clutter environment and multiple interfering targets situations.