Due to the complexities of indoor WiFi signal propagations, it is challenging to improve the performance of indoor fingerprint-based positioning techniques which is the main hot research in Internet of Things. Most existing methods have limited positioning accuracy, since they do not take the full advantage of the information available, i.e. timing information attached to the Received Signal Strength Indicator (RSSI) vector, and adopt the inappropriate training methods. This paper proposes an indoor localization method based on Convolutional Neural Network (CNN) by using time-series RSSI, termed CTSLoc, by taking into account the correlation among RSSI in time and space. A CNN model is used to extract the temporal fluctuation patterns of RSSI and learn the nonlinear mappings from the signal features with time and space to position coordinates. Finally the trained model is used to predict the user’s location. An extensive experiment has been carried out in a space with the size of nearly 1000 squared meters, and a comprehensive comparison with several existing methods indicates that CTSLoc attains a lower average localization error (i.e. 4.23 m) and more stable performance than those methods. The CTSLoc method performs relatively less dependent on the amount of data which also eliminates spatial ambiguity and reduces the effect of noise on localization.
|Number of pages||12|
|Publication status||Published - 19 Nov 2021|
Bibliographical noteFunding Information:
Supported by the National Natural Science Foundation of China (Grants No. 41761086 and 41871363), the Key Science-Technology Project of Inner Mongolia Autonomous Region (Grant No. 2021GG0163) and the Natural Science Foundation of Inner Mongolia Autonomous Region (Grant No. 2021ZD13 and 2019MS06030), the Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing and the Self-Topic/Open Project of Engineering Research Center of Ecological Big Data, Ministry of Education.
- Time-series RSSI
- WiFi indoor localization