一种基于神经网络的LFMCW雷达目标距离提取方法  

A METHOD BASED ON NEURAL NETWORK FOR LFMCW RADAR SYSTEM OBJECT RANGE SUPER-RESOLUTION ESTIMATION

作　　者：李阳[1] 黄敬禹[1] 冯正和[1] 

机构地区：[1]清华大学电子工程系微波与数字通信国家重点实验室,北京100084

出　　处：《红外与毫米波学报》2000年第6期425-429,共5页Journal of Infrared and Millimeter Waves

基　　金：国家863基金!(86 3-317-0 3-0 1-15 -99)资助项目&&

摘　　要：根据 L FMCW系统的特点 ,提出了一种通过对信号自相关矩阵进行秩 - 1分解的方法来替代传统的 FFT方法 ,以实现对目标距离的超分辨提取 .通过应用 Hopfield神经网络能量函数变换 ,将分解问题转化为一个简单的迭代问题来求解 .文中通过计算机仿真和硬件系统的实际测试研究了它的性能 ,并与 MUSIC、最大熵等其它谱估计方法做了比较 ,结果表明该方法具有更好的信噪比和分辨性能 .To replace the traditional FFT method to realize the object range super resolution estimation, an artificial neural network method was proposed to decompose signal's auto correlation matrix into the summation of rank 1 matrices, and convert the decomposition problem to an iterative one by using Hopfield neural network. The property of this method was investigated both theoretically and experimentally. And the results were compared with five other typical super resolution algorithms including MUSIC, etc., and it was found that the present method has a lower SNR threshold and higher range resolution.

关 键 词：线性调频连续波 神经网络 LFMCW系统 连续波雷达 目标提取 距离分辨率 信噪比 分辨性能 

分 类 号：TN958.94[电子电信—信号与信息处理]