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中文摘要：
针对无线电测向定位系统中解析算法定位误差较大的问题,提出了一种基于径向基（RBF）神经网络的双站只测向定位方法。利用RBF神经网络可逼近任意连续有界非线性函数的能力,通过样本学习,构建目标位置和测向站所测的来波方向角（DOA）映射模型。与目前解析算法相比,RBF定位模型不仅充分利用了测向站获取的所有二维DOA信息,而且利用神经网络的泛化特性、鲁棒特性,较好解决了测向误差对定位精度的影响。实验表明：该方法能有效提高定位精度。
英文摘要：
Aiming at the low location accuracy of the present radio direction-finding（DF） system,a novel algorithm based on the RBF neural network was proposed in this paper.The nonlinear mapping model between the target＇s position and estimating angles of the direction finders was established through training the radial basis function（RBF） neural network.Compared with the present arithmetic algorithm,the RBF model not only could make use of the DOA information from the direction finders,but also took full advantages of neural network＇s generalization and robustness,therefore it eliminated the DOA estimation error caused by location error.The experiments showed that the proposed approach improved the location accuracy significantly,and which had a broad application foreground.
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