Abstract
Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.
Highlights
Smartphone indoor positioning technology is a boost to the rapidly growing mobile location-based services (LBS) industry
The major challenges in this approach include the large errors associated with estimated distances and difficulties in system deployment, e.g., the trouble associated with obtaining the access points (APs) coordinates indoors
Motion dynamics are defined in this paper as position changes over time, which are represented by the distance moved and the movement heading
Summary
Smartphone indoor positioning technology is a boost to the rapidly growing mobile location-based services (LBS) industry. To mitigate the impact of RSSI variances, the position estimate can be augmented by motion information because the dynamics of indoor users are usually restricted, and their locations are highly correlated over time. Our positioning solution is a data fusion scheme, and it uses the smartphone built-in sensors to physically measure the motion dynamics information of indoor users. Two approaches have been proposed to use motion dynamics information for improving positioning accuracy. Particle filters can further improve positioning accuracy by applying more sophisticated non-linear and non-Gaussian models, as well as map information [15,31,32,33,34,35,36] The applicability of these solutions is restricted by the fidelity of the motion models. Other sensors and techniques of MDI estimation, e.g., vision-based techniques, will be integrated with HIPE in the future
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