| 基于神经网络的数字广播信号无线定位算法(英文) |
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| 引用本文： | 柯炜,吴乐南,殷奎喜.基于神经网络的数字广播信号无线定位算法(英文)[J].东南大学学报,2010,26(3):394-398. |
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| 作者姓名： | 柯炜 吴乐南 殷奎喜 |
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| 作者单位： | 柯炜,Ke Wei(东南大学信息科学与工程学院,南京,210096;南京师范大学物理科学与技术学院,南京,210097);吴乐南,Wu Lenan(东南大学信息科学与工程学院,南京,210096);殷奎喜,Yin Kuixi(南京师范大学物理科学与技术学院,南京,210097) |
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| 基金项目： | The National High Technology Research and Development Program of China （863 Program） （No.2008AA01Z227）; the Cultivatable Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China （No.706028） |
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| 摘 要： | 为了提高非视距(NLOS)环境下无线定位的准确性和可靠性,提出了一种利用数字广播信号进行移动台定位的神经网络方法.该方法利用神经网络的学习特性和逼近任意非线性函数的能力,建立到达时间(TOA)和到达时间差(TDOA)测量数据与坐标之间的映射关系.将神经网络的连接权值作为非线性动态系统的状态量进行估计,用基于扩展卡尔曼(EKF)的实时神经网络训练算法来训练多层感知器网络.由于基于EKF的训练算法给出的是连接权值的近似最小方差估计,其收敛性要优于误差反向传播(BP)算法.仿真结果表明,该算法在NLOS环境下有较高的定位精度,性能优于BP基的神经网络算法和最小二乘算法;且该定位方法不依赖于特定的NLOS误差分布,也无需视距(LOS)和非视距识别.
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| 关 键 词： | 数字广播信号 神经网络 扩展卡尔曼滤波 误差反向传播算法 多层感知器 |
Wireless location algorithm using digital broadcasting signals based on neural network |
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| Ke Wei,Wu Lenan,Yin Kuixi.Wireless location algorithm using digital broadcasting signals based on neural network[J].Journal of Southeast University(English Edition),2010,26(3):394-398. |
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| Authors: | Ke Wei Wu Lenan Yin Kuixi |
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| Institution: | 1School of Information Science and Engineering,Southeast University,Nanjing 210096,China;2School of Physics and Technology,Nanjing Normal University,Nanjing 210097,China) |
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| Abstract: | In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification. |
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| Keywords: | digital broadcasting signals neural network extended Kalman filter （EKF） backwards error propagation algorithm multilayer perceptron |
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