Improved Wi-Fi RSSI Measurement for Indoor Localization

Indoor localization based on Wi-Fi received signal strength indication (RSSI) has the advantage of low cost and easy implementation compared with a range of other localization approaches. However, Wi-Fi RSSI suffers from multipath interference in indoor dynamic environments, resulting in significant errors in RSSI observations. To handle this issue, a number of different methods have been proposed in the literature, including the mean method, Kalman filter algorithm, and the particle filter algorithm. It is observed that these existing methods may not perform sufficiently well in ever-changing dynamic indoor environments. This paper presents an algorithm to improve RSSI observations by using the average of a number of selected maximum RSSI observations. Smoothness index is employed to evaluate the quality of RSSI so as to select an appropriate number of RSSI observations. Experiments were conducted in four rooms and a corridor within an office building and the results demonstrate that the proposed method considerably outperforms the existing algorithms in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.