A Target Tracking Algorithm Using Grey Model Predicting Kalman Filter in Wireless Sensor Networks

Most of wireless sensor network target tracking algorithms depend on system state transition matrix, which is not offered in some cases. To solve the problem, a Grey Model predicting Kalman Filter (GMKF) target tracking algorithm is proposed. In GMKF, the problem is considered as a process of state location predicting and filtering. Firstly, target location is sensed and calculated by Received Signal Strength Indicator (RSSI), then the dynamic state-transition matrix is established by first order grey Model (GM (1, 1)). In addition, the observed and predicted target locations are filtered by Kalman Filter. Finally, the estimated target location is sent to sensing area to activate sensors. The simulation results show that GMKF algorithm improves tracking accuracy and reduces energy loss so that the network lifetime is prolonged.