High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

作     者：Baoxiong XU Jianxin YI Feng CHENG Ziping GONG Xianrong WAN Baoxiong XU;Jianxin YI;Feng CHENG;Ziping GONG;Xianrong WAN

作者机构：Electronic Information SchoolWuhan UniversityWuhan430072China 

出 版 物：《信息与电子工程前沿:英文版》 (Frontiers of Information Technology & Electronic Engineering)

年 卷 期：2023年第24卷第8期

页      面：1214-1230页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 081104[工学-模式识别与智能系统] 0826[工学-兵器科学与技术] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China(Nos.61931015,62071335,and 61831009) the Natural Science Foundation of Hubei Province,China(No.2021CFA002)。 

主　　题：Deep feedforward neural network Filter layer Passive radar Target tracking Tracking accuracy 

摘      要：In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.