基于深度学习GRU网络的UWB室内定位优化

为减少信号传输质量和距离估计算法等因素对定位精度的影响，将深度学习应用于超宽带（ultra wide band,UWB）室内定位系统，利用门控循环单元（gated recurrent unit,GRU）网络代替传统UWB室内定位系统中的三边测量过程，以提高UWB室内定位精度。在得到定位标签到基站的距离信息后，将距离信息输入GRU网络中，输出最终位置坐标。GRU作为循环神经网络（recurrent neural network,RNN）的变种，既含有RNN处理时序数据的优势，又解决了RNN中的长程依赖问题。对GRU网络模型中不同学习率、优化器、批量大小、网络层数、隐藏神经元数量参数进行调整和训练。结果表明，基于GRU网络模型的UWB室内定位系统显著提高了定位精度，平均定位误差为6.8 cm。

Optimization of UWB indoor localization based on deep learning GRU networks

In order to reduce the influence of signal transmission quality and distance estimation algorithm on the localization accuracy, deep learning was applied to the ultra wide band（UWB）indoor localization system, the gated recurrent unit（GRU） network was used to replace the traditional trilateral measurement, to improve the indoor localization accuracy of UWB. After get the distance from the localization tag to the base station, the distance information was input into the GRU network, and the final localization coordinates were output. As a variant of recurrent neural network（RNN）, GRU not only had the advantages of RNN in processing time series data, but also solved the long-term depenencies problem in RNN. The parameters of different learning rate, optimizer, batch size, network layer and the number of hidden neurons in the GRU networks model were adjusted and trained. The result showed the UWB indoor localization system based on the GRU networks model improved the localization accuracy significantly, the average localization error was 6.8 cm.