Deep Fuzzy System for Dual-Station Target Tracking With Azimuth and Doppler Measurements

作     者：Huang, Jianjun Geng, Xuehao Kang, Li Luo, Ge 

作者机构：Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518061 Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518061 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON FUZZY SYSTEMS》 (IEEE Trans Fuzzy Syst)

年 卷 期：2025年第33卷第4期

页      面：1287-1297页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Shenzhen Science and Technology Program [JCYJ20220818100004008] Guangdong Provincial Key Laboratory [2023B1212060076] 

主　　题：Fuzzy systems Target tracking Uncertainty Filtering Mathematical models Kalman filters Doppler effect Adaptation models Accuracy Noise Azimuth and Doppler uncertain models unscented filtering Wang-Mendel fuzzy model 

摘      要：To address the issues of excessive estimation error and unstable filtering caused by uncertainties in process noise and system models, we propose a Wang-Mendel fuzzy system (WMFS)-based unscented Kalman filter (UKF) algorithm for dual-station target tracking with azimuth and Doppler measurements. The algorithm leverages the strengths of WMFS in handling system uncertainties and complex modeling. During the derivation of the WMFS-UKF, the unscented transform (UT) framework is employed to tackle the nonlinear measurement problem, while the state transition function is reconstructed using a pretrained WMFS. By utilizing the historical states of the target and the expected outputs, the WM fuzzy inference system achieves more accurate state predictions and precise covariance estimates. This leads to significantly improved performance and enhanced stability in target tracking. Simulation experiments and real-data filtering experiment validate the algorithm s effectiveness and robustness in various target tracking scenarios.