Traditional Received Signal Strength Indication(RSSI)-based moving target localization and tracking generally employs tri-lateration/angulation techniques. Although this method is simple and easy to be implemented, it creates signi.cant errors in localization estimations due to nonlinear relationship between RSSI and distance. The Generalized Regression Neural Network (GRNN), a one-pass learning algorithm, is well known for its ability to train quickly on sparse data sets. Therefore, GRNN-based mobile Target Tracking(GMTT) is proposed in this paper. GMTT deals with high nonlinearity in RSSI’s target location relationship by using GRNN, then further refines these location estimates with the help of KF framework. Simulation results show that GMTT can effectively decrease the Root Mean Square Error(RMSE) of target localization compared with RSSI+KF.