Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1？m accuracy 70% of the time outperforming each system individually. 1. Introduction Inertial Navigation Systems (INSs) [1, 2] are self-contained inertial-sensors-based navigation systems that can work independently without any kind of help from an external navigation source. INS solutions utilize inertial sensors to provide navigation information continuously with time at high rates. Although INS provides good short-term accuracy without any external help, small sensors errors accumulate due to mathematical integration resulting in large drifting that grows without bounds. Additionally, if low-cost MEMS-grade [3] inertial sensors are considered, errors exhibit complex stochastic characteristics which are hard to model using linear estimator such as Kalman Filter because of the high inherent nonlinearity and randomness. In [4], a Reduced Inertial Sensors System (RISS) suitable for wheeled vehicles navigation was introduced. The aim was to reduce sensors cost and to simplify navigation equations reducing sources of errors. For this reasons, this paper utilizes an RISS system that provides navigation information for wheeled vehicles using only single vertically aligned gyroscope, and the vehicle speed sensor (odometer) (see Figure 1). This configuration is suitable for wheeled vehicles such as robots
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