Abstract
In order to provide the seamless navigation and positioning services for indoor environments, an indoor location based service (LBS) test bed is developed to integrate the indoor positioning system and the indoor three-dimensional (3D) geographic information system (GIS). A wireless sensor network (WSN) is used in the developed indoor positioning system. Considering the power consumption, in this paper the ZigBee radio is used as the wireless protocol, and the received signal strength (RSS) fingerprinting positioning method is applied as the primary indoor positioning algorithm. The matching processes of the user location include the nearest neighbor (NN) algorithm, the K-weighted nearest neighbors (KWNN) algorithm, and the probabilistic approach. To enhance the positioning accuracy for the dynamic user, the particle filter is used to improve the positioning performance. As part of this research, a 3D indoor GIS is developed to be used with the indoor positioning system. This involved using the computer-aided design (CAD) software and the virtual reality markup language (VRML) to implement a prototype indoor LBS test bed. Thus, a rapid and practical procedure for constructing a 3D indoor GIS is proposed, and this GIS is easy to update and maintenance for users. The building of the Department of Aeronautics and Astronautics at National Cheng Kung University in Taiwan is used as an example to assess the performance of various algorithms for the indoor positioning system.
Highlights
The location based service (LBS) subscriber base is forecasted to reach 680 million people worldwide, because LBS can be applied to many fields, such as local entertainment services, personal security applications and local traffic navigation systems [1]
In this paper several matching fingerprinting method algorithms are investigated to analyze their indoor positioning results, and the matching algorithms used in this paper included the nearest neighbor (NN) algorithm, the K-weighted neatest neighbor (KWNN) algorithm and the probabilistic approach based on the kernel method
The experiment results of this paper indicated that the K-weighted nearest neighbors (KWNN) algorithm with parameter K = 3 or 4 gave the best indoor positioning result
Summary
The location based service (LBS) subscriber base is forecasted to reach 680 million people worldwide, because LBS can be applied to many fields, such as local entertainment services, personal security applications and local traffic navigation systems [1]. Ni et al used radio frequency identification (RFID) technique to develop the LANDMARC indoor location sensing system in [4], and they pointed out that the matching algorithm, the nearest neighbor (NN) method, may get better positioning solution by selecting more reference points in an indoor positioning system. According to their experimental results, selecting four reference points could get more desirable results. The main contributions of this paper are the development of a low cost 3D GIS as a LBS test bed and its integration with the indoor positioning system using WSN.
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