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Article

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares

School of Information Science and Engineering, Xiamen University, Xiamen 361001, China
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Author to whom correspondence should be addressed.
Received: 2 April 2018 / Revised: 27 April 2018 / Accepted: 1 May 2018 / Published: 7 May 2018
For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m. View Full-Text
Keywords: INS; WiFi fingerprint; pre-processing techniques; MDTW; WLS INS; WiFi fingerprint; pre-processing techniques; MDTW; WLS
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MDPI and ACS Style

Chen, J.; Ou, G.; Peng, A.; Zheng, L.; Shi, J. An INS/WiFi Indoor Localization System Based on the Weighted Least Squares. Sensors 2018, 18, 1458. https://0-doi-org.brum.beds.ac.uk/10.3390/s18051458

AMA Style

Chen J, Ou G, Peng A, Zheng L, Shi J. An INS/WiFi Indoor Localization System Based on the Weighted Least Squares. Sensors. 2018; 18(5):1458. https://0-doi-org.brum.beds.ac.uk/10.3390/s18051458

Chicago/Turabian Style

Chen, Jian, Gang Ou, Ao Peng, Lingxiang Zheng, and Jianghong Shi. 2018. "An INS/WiFi Indoor Localization System Based on the Weighted Least Squares" Sensors 18, no. 5: 1458. https://0-doi-org.brum.beds.ac.uk/10.3390/s18051458

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