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A Hybrid Location Algorithm Based on BP Neural Networks for Mobile Position Estimation

机译:基于BP神经网络的移动位置混合定位算法。

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In this paper, we propose an efficient hybrid location algorithm with BP neural networks. We choose two-layer backpropagation network for data fusions and position estimation to improve hybrid location accuracy and efficiency with TOA/TDOA/AOA parameters in mobile communication networks. So the position estimation can be optimized by network parallel processing robustly,and the inaccuracy or fuzzy problem produced by conventional location algorithm can be overcome effectively. In the paper, the model of the data fusion with multi-parameters of TOA/TDOA/AOA is set up to optimize network hybrid location configuration, The Simulation results show that the algorithm with BP neural networks can work effectively.
机译:在本文中,我们提出了一种具有BP神经网络的高效混合定位算法。我们选择两层反向传播网络进行数据融合和位置估计,以提高移动通信网络中TOA / TDOA / AOA参数的混合定位精度和效率。因此,可以通过网络并行处理可靠地优化位置估计,并且可以有效地克服传统定位算法所产生的误差或模糊问题。建立了TOA / TDOA / AOA多参数数据融合模型,优化了网络混合位置配置,仿真结果表明,采用BP神经网络的算法可以有效地工作。

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