KALMAN FILTER ASSISTED FINE-GRAINED LOCATION ESTIMATION ALGORITHM FOR INDOOR WIRELESS SENSOR NETWORKS

This paper proposes a distributed location estimation methodology based on Received Signal Strength (RSS) that consists of two phases, range estimation phase and coordinate estimation phase. In range estimation phase the distance of the unknown node is computed based on the RSS measurements. The effect of channel loss and attenuation in the wireless medium are also considered in the proposed algorithm. The range estimation uses log normal shadowing path loss model and ITU indoor attenuation model. The ranging error is estimated and compromised using onedimensional Kalman filter. The number of iterations in Kalman filter is limited using Cramer Rao Bound (CRB) value. In the second phase the coordinates of the unknown node is estimated by tri-lateration techniques. The accuracy in lateration is improved by min-max algorithm. The RSS value is experimentally obtained in real-time indoor environment using zigbee series 1 RF module. The proposed algorithm is simulated and analyzed in MATLAB version 7. From the simulation results it is found that the proposed localization algorithm performs more efficient in terms of computational cost and accuracy