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.
|Number of pages||9|
|Publication status||Published - 8 Jul 2022|
Bibliographical noteFunding Information:
1. Organization name: National Natural Science Foundation of China Grant numbers: 42161070 Grant numbers: 41761086 Grant numbers: 41871363
3. Organization name: Key Science-Technology Project of Inner Mongolia Autonomous Region Grant numbers: 2021GG0163
2. Organization name: Natural Science Foundation of Inner Mongolia Autonomous Region of China Grant numbers: 2019MS06030 Grant numbers: 2021ZD13
© 2022 Elsevier B.V.
- Indoor location method
- Temporal Convolutional Network
- Time-series WiFi RSSI
- WiFi indoor localization