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An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

by He Xu 1,2,*, Ye Ding 1,2, Peng Li 1,2, Ruchuan Wang 1,2 and Yizhu Li 3
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Author to whom correspondence should be addressed.
Sensors 2017, 17(8), 1806;
Received: 15 June 2017 / Revised: 23 July 2017 / Accepted: 1 August 2017 / Published: 5 August 2017
(This article belongs to the Section Sensor Networks)
The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method. View Full-Text
Keywords: indoor positioning; RFID; Bayesian probability; K-Nearest Neighbor indoor positioning; RFID; Bayesian probability; K-Nearest Neighbor
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Xu, H.; Ding, Y.; Li, P.; Wang, R.; Li, Y. An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor. Sensors 2017, 17, 1806.

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