Abstract
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.
Highlights
Location-based services (LBS) are provided by a number of commercial companies, and it improves user satisfaction and offers convenience
We placed 117 reference tags positioned in 9 rows by 13 columns in the area of the Radio Frequency Identification (RFID) reader (Figure 6a shows part of the monitoring area) and 5 target tags
We placed 117 reference tags positioned in 9 rows by 13 columns in the monitoring area of the RFID reader
Summary
Location-based services (LBS) are provided by a number of commercial companies, and it improves user satisfaction and offers convenience. Nascimento Hitalo J.B. et al proposed a positioning algorithm [10] based on Bayes inference to locate objects in 3D WLAN indoor environments. This is a fingerprint technique and the average positioning error is about three meters. There are two types of Received Signal Strength indicator (RSS, many references refer to it as RSSI) based indoor positioning algorithms in RFID systems: triangulation and reference tags. The authors [32] present a novel passive RFID localization algorithm based on elliptical trilateration in smart home environments, where they obtained an average error of 16.08 cm for all objects. RFID indoor positioning algorithm is proposed where a Gaussian filter isSection used to abnormal and Bayesian estimation is used to improve positioning accuracy.
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