Accurate Target Tracking using Kalman Filtering and Location Estimation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs), is one of the most emerging technologies in recent years. Target tracking and location estimation is one of the most important tasks in wireless sensor networks. Target tracking using Bayesian filtering technique doesn�t concentrate much on filtering of noise in the signals. This reduces the accuracy of the target information. This paper addresses a Kalman filtering technique with prediction-correction step to remove the noise from a signal and to generate optimal estimate of desired quantities with the given set of measurements. Also it concentrates on location estimation of the sensor nodes in the network. The proposed Kalman filtering technique supports to solve the problem of trajectory estimation. Simulation results show that minimizes the energy consumption, missing rate, and localization error of the sensor nodes in the network.