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Sensors 2018, 18(9), 2869;

WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Authors to whom correspondence should be addressed.
Received: 3 July 2018 / Revised: 27 August 2018 / Accepted: 29 August 2018 / Published: 31 August 2018
(This article belongs to the Section Sensor Networks)
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WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading. View Full-Text
Keywords: channel state information (CSI); Random Forest; fingerprinting; indoor positioning; WiFi channel state information (CSI); Random Forest; fingerprinting; indoor positioning; WiFi

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Wang, Y.; Xiu, C.; Zhang, X.; Yang, D. WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest. Sensors 2018, 18, 2869.

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