基于神经网络CA/OS-CFAR检测方法

在杂波边缘和多目标的复杂环境下,建立性能稳定的自适应检测技术是提高恒虚警率处理能力的关键.针对单元平均恒虚警检测(cell averaging-constant false alarm rate)和有序统计量恒虚警检测(ordered statistic-constant false alarm rate)的优缺点,提出一种基于神经网络的检测方法(cell averaging/ordered statistic-constant false alarm rate).利用神经网络进行最优检测方法判断,根据选定的检测方法计算出检测阈值.通过训练计算初始阈值,采用神经网络分类并识别输入的类型.将该阈值与CA-CFAR和OS-CFAR计算结果相比较,并选用均匀杂波、多目标和杂波边缘环境的仿真案例进行测试.实验结果表明:该方法可在均值和非均匀的杂波背景中,能有效地进行最优检测方法判断.

CA/OS-CFAR Detection Method Based on Neural Network

In the complex environment of clutter edges and multiple targets,it is the key to improve the capability of CFAR processing to establish a stable adaptive detection technology.A method of CA/OS-CFAR detection based on neural network is proposed based on cell averaging-constant false alarm rate and cell averaging/ordered statistic-constant false alarm rate.Use the neural network to determine the optimal detection method,according to the selected detection method to calculate the detection threshold to improve the ability of radar detection target.The input of the neural network contains CA,OS-CFAR and the unit value to be measured.The initial threshold is calculated by training,and the type of input is classified and recognized by neural network.The threshold is compared with the results of CA-CFAR and OS-CFAR,and the optimal threshold is selected.This method is tested with a simulation case of homogeneous clutter,multiple targets and clutter edge environment.Experiments show that the method proposed in this paper can be used to determine the optimal detection method in the mean and non uniform clutter background.