期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 20, 期 12, 页码 7977-7991出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3089202
关键词
Location awareness; Probabilistic logic; Radial basis function networks; Wireless fidelity; Databases; Wireless communication; Estimation; Multi-floor indoor localization; RSSI fingerprinting; RBF network
资金
- National Natural Science Foundation of China [41504022]
- Shanghai Natural Science Foundation [20ZR1462000]
- Scientific and Technological Innovation Plan through the Shanghai Science and Technology Committee [20511103402]
This paper proposes a multi-floor indoor localization method using a radial basis function (RBF) network for floor detection and location estimation. The network is constructed based on a probabilistic algorithm, and supervised learning is conducted using Gaussian radial basis functions to ensure accuracy and stability in localization.
Received Signal Strength Indicator (RSSI) fingerprinting is known as the most concerned method for indoor localization as its high accuracy and low cost. Numerous RSSI based methods have shown their attractive performances, but the major drawback is the high dependency on the database. In this paper, we propose a multi-floor indoor localization method which includes floor detection and location estimation based on the radial basis function (RBF) network. To ensure the localization accuracy and stability, the network is constructed according to the probabilistic algorithm. Choosing Gaussian radial basis functions with appropriate widths, the network parameters can be initialized appropriately regardless of deficiency of RSSI data. By further conducting the supervised learning of RBF network, the network parameters will be effectively calibrated and updated, so as to achieve a better localization performance. In addition, a radio map quality evaluation criterion is proposed to conduct a comprehensive analysis and interpretation for the localization approach. Finally, experimental results of a publicly accessible dataset which includes multi-floors buildings verify that the performance of the proposed RBF network is superior to other commonly used methods.
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