Kalman Filtering Framework-Based Real Time Target Tracking in Wireless Sensor Networks Using Generalized Regression Neural Networks

Traditional received signal strength indicators (RSSI's)-based moving target localization and tracking using wireless sensor networks (WSN's) generally employs lateration/angulation techniques. Although this method is a very simple technique but it creates significant errors in localization estimations due to nonlinear relationship between RSSI and distance. The generalized regression neural network (GRNN) being a one-pass learning algorithm is well known for its ability to train quickly on sparse data sets. This paper proposes an implementation of GRNN as an alternative to this traditional RSSI-based approach, to obtain first location estimates of single target moving in 2-D in WSN, which are then further refined using Kalman filtering (KF) framework. Two algorithms namely, GRNN + KF and GRNN + unscented KF (UKF) are proposed in this paper. The GRNN is trained with the simulated RSSI values received at moving target from beacon nodes and the corresponding actual target 2-D locations. The precision of the proposed algorithms are compared against traditional RSSI-based, GRNN-based approach as well as other models in the literature such as traditional RSSI + KF and traditional RSSI + KF algorithms. The proposed algorithms demonstrate superior tracking performance (tracking accuracy in the scale of few centimeters) irrespective of nonlinear system dynamics as well as environmental dynamicity.