利用RBF神经网络实现对非相参积累检测系统中阈值的精确估计  

Accurate Estimation of Threshold in Non-coherent Integration System Based on RBF Neural Networks

作　　者：杨军[1] 马晓岩[1] 万山虎[2] 张荣华[1] 

机构地区：[1]空军雷达学院信息工程系,武汉430019 [2]空军雷达学院科研部,武汉430019

出　　处：《空军工程大学学报（自然科学版）》2004年第3期24-27,共4页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家"8 6 3"高技术研究发展计划资助项目 (2 0 0 2AA135 32 0 )

摘　　要：在非相参积累检测系统中 ,容易得到雷达虚警概率关于阈值的解析表达式 ,但难以得到阈值关于虚警概率的解析表达式 ,利用径向基函数 (RBF)神经网络具有良好的逼近任意非线性映射和快速收敛的特点 ,提出了一种精确估计阈值的RBF神经网络方法。仿真结果表明 。It is easy to yield the analytical expression of the probability of false alarm with respect to the threshold, but hard to obtain the analytical expression of the threshold with respect to the probability of false alarm. By making use of the perfect properties of radial basis function (RBF) neural networks, such as approaching arbitrary non-linear mapping and quick convergence, a new scheme based modified RBF neural networks is proposed in obtaining threshold estimation. Simulation results show that the proposed scheme is of higher accuracy in threshold estimation.

关 键 词：径向基函数 神经网络 阈值 检测概率 

分 类 号：TN015[电子电信—物理电子学]