An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.
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TL;DR: The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm and the average error in location estimation is about 15 cm using the method.
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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.
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References
LANDMARC: indoor location sensing using active RFID
TL;DR: This paper presents LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings and demonstrates that active RFID is a viable and cost-effective candidate for indoor location sensing.
2.8K
LANDMARC: indoor location sensing using active RFID
Lionel M. Ni,Yunhao Liu,Yiu Cho Lau,Abhishek Pramod Patil +3 more
- 23 Mar 2003
TL;DR: LANDMARC is presented, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings that improves the overall accuracy of locating objects by utilizing the concept of reference tags.
RFID handbook : fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication
Klaus Finkenzeller,Dörte Müller +1 more
- 18 Jun 2010
TL;DR: The Third Edition of RFID: The Architecture of Electronic Data Carriers focuses on the architecture of Transponders and Contactless SmartCards, as well as security and selection Criteria for RFID Systems, which addresses attacks on RFID systems.
1.5K
WiFi-based indoor positioning
Chouchang Yang,Huai-Rong Shao +1 more
TL;DR: Simulation results show that the WiFi-based positioning approach can achieve 1 m accuracy without any hardware change in commercial WiFi products, which is much better than the conventional solutions from both academia and industry concerning the trade-off of cost and system complexity.
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Phase based spatial identification of UHF RFID tags
Pavel Nikitin,Rene Martinez,Shashi Ramamurthy,Hunter Leland,Gary Neal Spiess,K.V.S. Rao +5 more
- 14 Apr 2010
TL;DR: In this paper, the authors give an overview of spatial identification of modulated backscatter UHF RFID tags using RF phase information, and describe three main techniques based on PDOA (phase difference of arrival): TD (Time Domain), FD (Frequency Domain), and SD (Spatial Domain).
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