An Adaptive Location Estimator Based on Alpha-Beta Filtering for Wireless Sensor Networks

This paper presents a new scheme for positioning and tracking mobile nodes based on adaptive weighted interpolation and alpha-beta (alpha-beta) filtering in wireless sensor networks. The proposed positioning method formulates location estimation as a weighted least squares problem, which can be solved in an iterative, decentralized manner. With such estimated location information, an alpha-beta tracking algorithm is further employed at a central processor to improve the location accuracy. As compared with the Kalman filtering approach, the proposed alpha-beta tracking method achieves reasonably good performance with much lower computational complexity and no need of exact information about the state and measurement noise parameters. Computer simulation results show that more than 90 percent of the estimated locations have error distances less than 2.5 meters.