Mobile cooperative tracking with RSSI ranging in EKF algorithm for indoor wireless sensor network

Abstract

Target tracking is an application in WSN (Wireless Sensor Network) that estimating directly the mobile target position. However, in indoor location with large coverage area it is difficult to get the precision estimation, especially using RSSI (received strength signal indicator) with poor accuracy. The condition of an indoor location with many walls, obstacle, pillar, doors, multipath fading, and attenuation is influenced to the RSSI measurement in determining the distance estimation from PLE (path loss exponent) value. In this paper, we propose mobile cooperative tracking based on extended RSSI ranging to obtain localization information from three nearest reference devices continuously. RSSI measurement has data fluctuating and nonlinear characteristic. Using EKF (extended Kalman filter) algorithm the error estimation position from the mobile cooperative tracking result could be reduced as well. The combination of mobile cooperative tracking and EKF algorithm for indoor wireless sensor network yield error reduction percentage 47.47%, with MSE (mean square error) 1.09 m. Although this combination has the capability to reduce error estimated position, the time computation is in 120.74 seconds longer than mobile cooperative tracking which only needs 41.73 seconds.