文件名称:TOAAOA
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- 上传时间:2013-05-08
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为了减小NLOS 传播的影响,基于几何结构的单次反射统计信道模型,该文提出一种NLOS 环境下的TOA/AOA 定位算法。利用RBF 神经网络较快的学习特性和逼近任意非线性映射的能力,对NLOS 传播的误差进行修正以减小NLOS 传播的影响,再利用最小二乘(LS)算法进行定位,从而提高系统的定位精度。-In order to mitigate the effect of NLOS propagation, based on the Geometry Based Single- Bounced
(GBSB)statistical model, a TOA/AOA location algorithm based on the RBF neural network is proposed. The
fast study and non-linear approach capacity of the neural network is made use of to correct the error of NLOS
propagation, then the position is calculated by Least-Square (LS) algorithm to improve the location accuracy.
(GBSB)statistical model, a TOA/AOA location algorithm based on the RBF neural network is proposed. The
fast study and non-linear approach capacity of the neural network is made use of to correct the error of NLOS
propagation, then the position is calculated by Least-Square (LS) algorithm to improve the location accuracy.
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一种NLOS环境下的TOA_AOA定位算法.pdf
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