An Adaptive Location Estimator Based on Kalman Filtering for Wireless Sensor Networks

In this paper, we present a positioning and tracking scheme based on adaptive weighted interpolation and Kalman filtering for wireless sensor networks. The proposed positioning method formulates location estimation as a weighted least squares problem by taking weights based on the reliability of distance estimation. This method can be realized in an iterative, decentralized manner to improve both bandwidth and energy efficiencies. To improve the location accuracy, a Kalman filter is employed at the central server to track variations of the location estimate computed from the proposed positioning method. As compared with a previous positioning approach based on the projection onto convex sets, the proposed scheme has faster convergence speed and better location accuracy. Computer simulation results show that more than 90 percent of the location estimates computed from the proposed approach have error distances less than 2.5 meters.