TTSL: An indoor localization method based on Temporal Convolutional Network using time-series RSSI
TTSL: An indoor localization method based on Temporal Convolutional Network using time-series RSSI
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TTSL：一种基于时间序列RSSI的时间卷积网络的室内定位方法
DOI：
10.1016/j.comcom.2022.07.003
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发表时间：
2022-07-21
影响因子：
6
通讯作者：
Tawfik, Hissam
中科院分区：
文献类型：
--
作者：
Jia, Bing;Liu, Jingbin;Tawfik, Hissam
现有的室内定位方法主要针对单个接收信号强度指示（RSSI）的研究，没有充分利用RSSI所附带的时间信息，定位精度受到限制。针对RSSI信号在时间和空间上的相关性，利用信号在连续位置上的时间和空间特性，提出了基于时间卷积网络（TCN）的时间序列定位（TTSL）方法。利用神经网络模型提取连续位置信号的时间波动特征，学习信号特征与时间、空间到位置坐标的非线性映射关系。实现了RSSI时间与轨迹中位置信息的关联，将离散的定位任务转化为连续时间序列的特征发现任务。在约1000平方米的空间内进行了大量实验，并与现有方法进行了全面比较。TTSL的平均定位误差为3.73 m，性能比现有的方法更稳定。TTSL方法对数据量的依赖性相对较小，消除了空间模糊，显著降低了噪声对定位结果的影响。
The existing indoor location methods are mainly oriented towards the study of single Received Signal Strength Indication (RSSI), which does not make full use of the time information attached to RSSI, so the location accuracy is limited. In this paper, considering the correlation of RSSI in time and space, Temporal Convolutional Network (TCN) based Time Series Localization (TTSL) method is proposed by using the temporal and spatial characteristics of signals in continuous locations. The neural network model is used to extract the time fluctuation characteristics of signals in continuous locations, and the nonlinear mapping relationship between signal characteristics and time and space to location coordinates is learned. The correlation of RSSI time and location information in the trajectory is realized, and the discrete location task is transformed into a continuous time series feature discovery task. A large number of experiments were carried out in the space of approximately 1000 square meters, and a comprehensive comparison was made with the existing methods. The average location error of TTSL was 3.73 m, and the performance is found to more stable than the existing methods. The TTSL method has a relatively small dependence on data volume, eliminates spatial ambiguity, and significantly reduces the influence of noise on location results.