Moving sensor nodes´ tracking by WT-UKF in Wireless Sensor Networks

Tracking moving sensor node is a key point in Wireless Sensor Networks (WSN). However, it´s difficult to achieve high tracking accuracy quickly because many problems should be solved. After analyzing those fundamental problems, Unscented Kalman Filter (UKF) is established to estimate the moving sensor node´s localization and environment argument simultaneously. To adapt for the case of colored measurement noise, the UKF model is improved to form a time-lag UKF model with Gauss Noise based on the first-order Auto Regressive (AR) model of the colored noise. To decrease the influence of dynamic variance of measurement noise in the time-lag UKF model, Wavelet Transformation (WT) is used to estimate the standard deviation of the measurement noise on-line fast, and the new model is named WT-UKF. In simulations, results show that WT-UKF is always better than UKF when the number of the measurement values analyzed by WT in one interval is set properly.