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An Indoor Positioning Method Based on CSI by Using Features Optimization Mechanism With LSTM

机译：利用LSTM的特征优化机制基于CSI的室内定位方法

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摘要

In recent years, the research on indoor positioning has received extensive attention, especially the positioning method without portable devices. In this paper, we propose a positioning method by optimizing the channel state information (CSI) amplitude and phase data feature ratio. In the off-line training stage, for each experimental scenario, we select different proportions of amplitude and phase data to form different data sets, and train with LSTM neural network. By comparing the results of the training, the model under the optimal feature ratio is obtained. In the on-line localization phase, the model predicts the regression of the test points by calling the prediction function and outputs the results. Experiments are conducted in open environment and complex laboratory environment to evaluate the performance of the method, and we compare this method with the current state-of-the-art indoor positioning solutions, such as: DeepFi, FILA, RNN and EC-SVM. Experimental results are presented to confirm that our method can effectively improve the accuracy of indoor positioning.
机译：近年来，对室内定位的研究已经受到广泛的关注，尤其是没有便携式设备的定位方法。在本文中，我们通过优化信道状态信息（CSI）幅度和相位数据特征比来提出定位方法。在离线培训阶段，对于每个实验场景，我们选择不同比例的幅度和相位数据，以形成不同的数据集，并用LSTM神经网络列车。通过比较训练的结果，获得了最佳特征比下的模型。在在线定位阶段，模型通过呼叫预测函数来预测测试点的回归并输出结果。实验在开放环境和复杂的实验室环境中进行，以评估该方法的性能，并将这种方法与当前的最先进的室内定位解决方案进行比较，例如：DeepFi，Fila，RNN和EC-SVM。提出了实验结果以确认我们的方法可以有效地提高室内定位的准确性。

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