Abstract
The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.
Highlights
The accuracy and reliability of a localization system can greatly affect the performance for location-based applications associated with the ubiquitous and pervasive systems, including location-based services (LBS), wireless social networks, Internet-of-Things (IoT), etc
The information measured by built-in inertial measurement units (IMUs) sensors in the iPhone can offer a reliable heading data for the LBS applications implemented on the iPhone; if the user performs our positioning system on an Android phone, the heading information is obtained from Android libraries with regard to the motion sensing [41]
Most indoor pedestrian positioning methods are affected by considerable errors owing to the RF signals that can vary over space and time, the user’s movements, and the use of cheap microelectromechanical systems (MEMS)
Summary
The accuracy and reliability of a localization system can greatly affect the performance for location-based applications associated with the ubiquitous and pervasive systems, including location-based services (LBS), wireless social networks, Internet-of-Things (IoT), etc. By comparing RSS values at neighboring positions, GIFT generates a fingerprint map based on RSS gradients called Gmap It estimates the position of users by integrating the RSS measurements and motion detection information using an extended particle filter; that is, their locations are predicted through mobility sensing results and are updated based on the comparison between. RSS fingerprints are jointly considered using a specialized particle filter It learns parameters of step length model, calibrates RF signal data owing to heterogeneous devices, and estimate the user location accurately by solving a convex optimization problem. PF-based localization methods along with the proposed algorithm in this paper
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