西安电子科技大学学报（自然科学版）2025，Vol.52Issue(1)：37-49,13.DOI:10.19665/j.issn1001-2400.20241015
基于SLSTM网络的两级修正机动目标跟踪方法
Two-level modified maneuvering target tracking method based on the SLSTM network
摘要
Abstract
In terms of maneuver model modeling,traditional maneuvering target tracking methods achieve matching between the model and the real motion of the target through adaptive interaction of the model set.When tracking non-cooperative targets,the maneuvering state changes at any time and the maneuvering forms are diverse.When the limited models in the model set cannot accurately represent its real motion,the tracking performance will degrade.This paper integrates the two level neural network of model correction and state correction into the filtering recursion process,and proposes a two-level modified maneuvering target tracking method(TLM-MTT)based on the stacked long short-term memory(SLSTM)network.The first-level model correction network perceives the maneuver of the target in real time,adjusts the model parameters,and realizes accurate modeling of the maneuver model.The second-level state correction network compensates the state estimation in real time to improve the accuracy of the filter output.The network is trained offline,and the trained network is used for online real-time tracking.Compared with traditional methods and other intelligent filtering methods,this method has a better tracking performance for high-maneuvering target tracking.关键词
目标跟踪/长短时记忆网络/卡尔曼滤波Key words
target tracking/long short-term memory(LSTM)/kalman filter分类
电子信息工程引用本文复制引用
汪晋,苏洪涛,汪圣利,陆超..基于SLSTM网络的两级修正机动目标跟踪方法[J].西安电子科技大学学报（自然科学版）,2025,52(1):37-49,13.基金项目
国家自然科学基金(62201418,62192714) （62201418,62192714）
