FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning

Abstract

As indoor positioning has been widely utilized for many applications of the Internet of Things, the Received Signal Strength Indication (RSSI) fingerprint has become a common approach to distance estimation because of its simple and economical design. The combination of a Gaussian filter and a Kalman filter is a common way of establishing an RSSI fingerprint. However, the distributions of RSSI values can be arbitrary distributions instead of Gaussian distributions. Thus, we propose a Fouriertransform Fuzzyc-means Kalmanfilter (FFK) based RSSI filtering mechanism to establish a stable RSSI fingerprint value for distance estimation in indoor positioning. FFK is the first RSSI filtering mechanism adopting the Fourier transform to abstract stable RSSI values from the low-frequency domain. Fuzzy C-Means (FCM) can identify the major Line of Sight (LOS) cluster by its fuzzy membership design in the arbitrary RSSI distributions, and thus FCM becomes a better choice than the Gaussian filter for capturing LOS RSSI values. The Kalman filter summarizes the fluctuating LOS RSSI values as the stable latest RSSI value for the distance estimation. Experiment results from a realistic environment show that FFK achieves better distance estimation accuracy than the Gaussian filter, the Kalman filter, and their combination, which are used by the related works.