Sensors, Vol. 19, Pages 3907: Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Sensors, Vol. 19, Pages 3907: Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks Sensors doi: 10.3390/s19183907 Authors: Kwangjae Sung Hyung Kyu Lee Hwangnam Kim 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 empl...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research