With the ever-increasing demand for location-based services in the indoor environments, Wi-Fi-based positioning technology has attracted much attention in decades of years because of its ubiquitous deployment and low cost. There is the fact that Wi-Fi signal not only changes with the distance away from the target, but also changes with time. To improve positioning accuracy and robustness, we consider both the spatial relation and temporal sequential relation simultaneously, and propose a spatial–temporal positioning algorithm that combines residual network and long short-term memory (LSTM) network. In this algorithm, to avoid the degradation problem, we adopt the residual-based network to extract the spatial features of the Wi-Fi signal at the same time slice. Furthermore, the LSTM is used to extract temporal features of the Wi-Fi signal among successive time slices. Finally, a fully connected layer is used to obtain the final location estimation. Extensive experiments on the IPIN2016 data sets demonstrate that our proposed algorithm can obtain 4.93-, 5.40-, 3.20-, and 4.98-m average positioning error on the UAH, CAR, UJIUB, and UJITI subdata set, respectively. The experimental results show that our proposed algorithm outperforms other state-of-the-art positioning algorithms with better accuracy and robustness.

中文翻译：

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使用残差网络和 LSTM 的时空定位算法

随着室内环境对基于位置服务的需求不断增加，基于Wi-Fi的定位技术因其无处不在的部署和低成本而在几十年来备受关注。有一个事实，Wi-Fi 信号不仅会随着距离目标的距离而变化，还会随着时间而变化。为了提高定位精度和鲁棒性，我们同时考虑空间关系和时间序列关系，并提出了一种结合残差网络和长短期记忆（LSTM）网络的时空定位算法。在该算法中，为了避免退化问题，我们采用基于残差的网络同时提取Wi-Fi信号的空间特征。此外，LSTM 用于在连续时间片中提取 Wi-Fi 信号的时间特征。最后，使用全连接层来获得最终的位置估计。在 IPIN2016 数据集上的大量实验表明，我们提出的算法可以分别在 UAH、CAR、UJIUB 和 UJITI 子数据集上获得 4.93-、5.40-、3.20- 和 4.98-m 的平均定位误差。实验结果表明，我们提出的算法优于其他最先进的定位算法，具有更好的准确性和鲁棒性。分别。实验结果表明，我们提出的算法优于其他最先进的定位算法，具有更好的准确性和鲁棒性。分别。实验结果表明，我们提出的算法优于其他最先进的定位算法，具有更好的准确性和鲁棒性。