Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor

• Published in: ISPRS international journal of geo-information Vol. 10; no. 10; p. 706
• Main Authors: Cao, Hongji, Wang, Yunjia, Bi, Jingxue, Sun, Meng, Qi, Hongxia, Xu, Shenglei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Accuracy; Algorithms; dual-band fingerprint; Fingerprints; Gaussian process; Gaussian process regression; K-nearest neighbors algorithm; Localization; Neighborhoods; Normal distribution; normalization; Radio frequency identification; Random variables; Root-mean-square errors; Routers; Signal strength; Stability; Wi-Fi
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi10100706

Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. In the offline stage, the received signal strength (RSS) measurements of the 2.4 GHz and 5 GHz signals at the reference points (RPs) are collected and normalized to generate the online dual-band fingerprint, a special fingerprint for dual-band Wi-Fi. Then, a dual-band fingerprint database, which is a dedicated fingerprint database for dual-band Wi-Fi, is built with the dual-band fingerprint and the corresponding RP coordinates. Each dual-band fingerprint constructs its positioning model with the GPR algorithm based on itself and its neighborhood fingerprints, and its corresponding RP coordinates are the label of this model. The neighborhood fingerprints are found by the spatial distances between RPs. In the online stage, the measured RSS values of dual-band Wi-Fi are used to generate the online dual-band fingerprint and the 5 GHz fingerprint. Due to the better stability of the 5 GHz signal, an initial position is solved with the 5 GHz fingerprint and the KNN algorithm. Then, the distances between the initial position and model labels are calculated to find a positioning model with the minimum distance, which is the optimal positioning model. Finally, the dual-band fingerprint is input into this model, and the output of this model is the final estimated position. To evaluate the proposed method, we selected two scenarios (A and B) as the test area. In scenario A, the mean error (ME) and root-mean-square error (RMSE) of the proposed method were 1.067 and 1.331 m, respectively. The ME and RMSE in scenario B were 1.432 and 1.712 m, respectively. The experimental results show that the proposed method can achieve a better positioning effect compared with the KNN, Rank, Coverage-area, and GPR algorithms.