Accelerometer assisted robust wireless signal positioning based on a hidden Markov model

Reliable and accurate indoor positioning remains nowadays as one of the greatest challenges in the area of personal navigation and location based services (LBS). This manuscript proposes methods to improve the accuracy and robustness of indoor positioning using signal strength measurements of Wireless Local Area Networks (WLAN), and presents three aspects of contributions. First, the Weibull function is employed to represent the distribution of the signal strength over time. Thus, the impact of the signal strength variation on the fingerprinting database is mitigated, and fewer samples are required for training the database. Second, the accelerometer sensor is utilized to provide the pedestrian dynamics information, which is used to improve the positioning accuracy and reliability. Lastly, hidden Markov model (HMM) based particle filters are performed to compute the positioning solution through combining the signal strength measurements with the pedestrian dynamics information. Through the experimental evaluation of three scenarios, the proposed methods were found to improve significantly the accuracy and robustness of WLAN positioning. Due to their affordable computational load, the positioning methods proposed can be implemented for indoor navigation on mass-market mobile devices without any extra cost requirements.