基于RBF网络的扫描体制雷达DOA估计方法

针对传统扫描体制雷达无法分辨半功率波束宽度内存在多目标的问题,利用阵列信号处理的思想,把RBF神经网络理论应用于机扫雷达的DOA高分辨估计.首先给出了扫描体制雷达DOA估计的信号模型,提出了一种基于RBF网络实现扫描体制雷达DOA高分辨估计的SRBF算法.然后针对RBF网络存在的学习收敛速度慢等问题,给出了基于模糊学习矢量量化(Fuzzy Algorithm for Learning Vector Quantization,FLVQ)的网络学习算法,FLVQ方法采用模糊C均值方法中的模糊权重函数在线自适应调整,来确定输入和中心之间的权值,使得网络具有更高的非线性逼近性能和高效的收敛性.理论分析和仿真结果均表明SRBF网络具有快速准确的DOA估计能力,算法便于工程实现,具有较高的实用价值.

DOA Estimation Method for Scanning Radar Based on RBF Network

In view of the problem that multiple targets are present in the main-lobe of the rotating radar, a method based on the idea of spatial spectrum estimation is proposed, which apples the RBF neural network theory to scanning radar. First a signal model of scanning radar system DOA estimation is presented and a high resolution DOA estimation algorithm is advanced based on RBF network. Then the lower speed of learning and convergence for conventional method is analyzed, thereafter a network learning algorithm based on fuzzy algorithm for learning vector quantization is proposed. In this method, the fuzzy weigh function of the mean of fuzzy-C and the online adaptive adjustment is adopted to determine the weigh between the input and the centre, which makes the network possess a better nonlinear approach performance and high-efficiency convergence. Both the theoretical analysis and the simulation results indicate that this network is fast and exact in estimation performance. The algorithm is effective and is of higher practical value.