Xingchuan Liu

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Providing a seamless, ubiquitous and reliable positioning system based on smartphones plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor/outdoor seamless positioning is still a challenge because of the(More)
Reliable and accurate vehicle position information is important for autonomous systems. The increased popularity of wireless networks has enabled the development of positioning techniques that rely on WLAN signal strength. Fingerprint architecture is one of the most viable solutions for Received Signal Strength (RSS)-based positioning. The most challenging(More)
Recently, fingerprint positioning systems for urban areas using the existing wireless-LAN (WLAN) play important role in ubiquitous applications. The most challenge in fingerprint positioning is how to reduce the number of training samples and high time consumption with no significant degradation in location accuracy. To deal with this problem, we propose a(More)
In this paper, a novel algorithm called Receding Horizon Kalman Particle Filter (RHKPF) has been proposed and is applied to our improved fingerprint-based WLAN vehicle positioning system. The RHKPF is a particle filter that the optimal importance density is approximated by incorporating the most current measurement through a Receding Horizon Kalman Filter(More)
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