Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be\r\nused as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On\r\nthe other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning\r\nindoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and\r\nnoisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced\r\nInertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts.\r\nSecond, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy\r\npower to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state\r\nestimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and\r\nthe scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1m\r\naccuracy 70% of the time outperforming each system individually.
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