基于Grubbs准则的改进的2D CA-CFAR

An improved 2D CA-CFAR based on Grubbs criterion

在不同程度的噪声存在下,现代雷达通过恒虚警率(constant false alarm rate, CFAR)检测算法最大化提升目标的检测概率。针对车载毫米波雷达目标检测过程中传统2维单元均值类恒虚警率(two-dimensional cell averaging CFAR,2D CA-CFAR)检测算法在相邻多目标条件下出现的目标遮蔽问题,提出了一种基于格拉布斯(Grubbs)准则的2D CA-CFAR的改进算法。该方法利用Grubbs准则剔除参考窗中的奇异值和干扰目标,从而获得更加准确的检测阈值。仿真结果表明:改进的算法极大地降低了多目标环境中传统2D CA-CFAR检测器的“遮盖效应”,验证了所提算法的有效性。

In the presence of varying levels of noise, modern radar maximizes the probability of target detection by means of constant false alarm rate(CFAR) detection algorithms.Aiming at the problem that the conventional two-dimensional cell averaging CFAR(2D CA-CFAR) detection algorithm tends to have target occlusion in the presence of multiple adjacent targets, an improved algorithm for 2D CA-CFAR based on Grubbs criterion was proposed.The method used the Grubbs criterion to remove singular values and interfering targets from the reference window to obtain a more accurate detection threshold.Simulation results show that the improved algorithm significantly reduces the “masking effect” of the conventional 2D CA-CFAR detector in a multi-target environment, which validates the effectiveness of the proposed algorithm.