Abstract
The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.
Highlights
With the development of the Internet of Things (IoT) and the popularization of mobile devices such as smart phones, a variety of mobile applications have changed people’s lifestyles tremendously
The results show that our proposed method can achieve better localization accuracy when only a small quantity of received signal strength (RSS) measurements is available
This paper investigates the problem of localization arising from insufficient RSS measurements, that is, the missed access points (APs) problem and the RSS measurement noise problem
Summary
With the development of the Internet of Things (IoT) and the popularization of mobile devices such as smart phones, a variety of mobile applications have changed people’s lifestyles tremendously. These applications enable users to access a plenty of services at any time in any place and often use their location information in order to provide them with personalized experiences. The global positioning system (GPS) can achieve meterlevel accuracy in outdoor environments. GPS works poorly inside buildings due to the signal attenuation caused by roofs, walls, and other objects. Since wireless information access is widely available, many of these approaches tap into wireless signals for estimating locations
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