基于接收信号强度指示 (RSSI)的移动目标定位和跟踪常采用三边或角度测量定位技术。尽管该技术简单，易实施，但由于 RSSI值与距离间的非线性关系，它们容易导致较大的定位误差。通用回归神经网络 (GRNN) 能够快速训练稀疏数据集。提出基于 GRNN的移动目标跟踪 (GMTT)算法，该算法依据 GRNN处理 RSSI与目标位置间的非线性关系，利用卡尔曼滤波 (KF)修正目标位置。仿真实验结果表明，相比于 RSSI+KF，GMTT算法可以有 效地降低目标定位的根均方误差。

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.