The Accurate Estimations of Distances Among Nodes in Wireless Sensor Networks in a Complex Environment Based on an Adaptive Kalman Filter

The accurate estimations of distances among nodes in wireless sensor networks(WSN) are premises for the accurate estimations of nodes' positions and the accurate reconstruction of the networks' topology. In a complex environment, nodes stochastically move, and the variance of state noise is unknown and variable, and obstacles possibly exist among nodes. Now, the accurate estimations of distances among nodes in the WSN in a complex environment are still unresolved. In this paper, an adaptive Kalman filter(AKF) is used to solve the accurate estimations of distances among nodes in the WSN. Through simulations, the results indicate that the AKF is capable of accurate estimations of distances among nodes. Therefore, the AKF is a very effective method to solve the accurate estimations of distances among nodes in the WSN in a complex environment.