Smartphone positioning in large environments by sensor data fusion, particle filter and FCWC
The design of an offline algorithm for smartphone based indoor localization is outlined, discussed and evaluated. The algorithm performs dead reckoning using accelerometer compass and gyroscope data. Accelerometer data is used to detect step events. Compass and gyroscope are used for heading estimation. Drift compensation and step length estimation is performed by a particle filter using map information. Floor detection is based on position and RSSI (Radio signal strength indicator) evaluation. For the RSSI positioning, the FCWC (fingerprint calibrated weighted centroid) method is applied. A particular feature of the algorithm is its backtracking capable particle filter. Particles comprise state information and references which allow backtracking of the particles history and a posteriori elimination of failed particles. This can lead to a significant accuracy increase for cases, where the position information is not needed in real time. The method is related to particle backward smoothers, as well as loop closing. In the paper the approaches for the step counter, the gyro magnetic sensor fusion, the particles and the particle filter, the backtracking algorithm, the RSSI positioning and floor estimation as well as the results are described and discussed.
2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)