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基于RBF神经网络的AOA定位算法

AOA location algorithm based on RBF neural network

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【Author】 MAO Yong-yi1,2,3,LI Ming-yuan4,ZHANG Bao-jun3(1.National Time Service Center, Chinese Academy of Sciences, Xi’an Shaanxi 710600, China;2.Graduate School, Chinese Academy of Sciences, Beijing 100039, China;3.Department of Electronic and Information, Xi’an University of Post and Telecommunications, Xi’an Shaanxi 710061, China;4.School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an Shaanxi 710049, China)

【机构】中国科学院国家授时中心；西安交通大学电子与信息工程学院；西安邮电学院电子与信息工程系西安710600；中国科学院研究生院；北京100039；西安邮电学院电子与信息工程系；西安710061；西安710049；

【摘要】基于几何结构的单次反射统计信道模型,提出了基于RBF神经网络的AOA定位算法。应用RBF神经网络对非视距传播(NLOS)误差进行修正,然后利用最小二乘(LS)算法进行定位。仿真结果表明:该算法减小了NLOS传播的影响,提高了系统的定位精度,性能优于LS算法。更多还原

【Abstract】 According Geometrically Based Single-Bounced (GBSB) statistical model, an Angle Of Arrival (AOA) location algorithm based on RBF neural network was proposed. The RBF neural network was used to correct the error of Non-Line-Of-Sight (NLOS)propagation, then the position was calculated by Least-Square algorithm (LS). The simulation results indicate that the location accuracy is significantly improved and the performance of this algorithm is better than that of LS algorithm.更多还原

【关键词】电波到达角；非视距传播；最小二乘法；神经网络；
【Key words】 Angle Of Arrival (AOA)； Non-Line-Of-Sight (NLOS)； Least Squares algorithm (LS)； neural network；

【基金】陕西省自然科学基金资助项目(2004F12)

【文献出处】计算机应用 ,Journal of Computer Applications , 编辑部邮箱 ,2008年01期

【分类号】TP183;TN929.5
【被引频次】20
【下载频次】477

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