信号处理2025，Vol.41Issue(7)：1190-1205,16.DOI:10.12466/xhcl.2025.07.005
一种基于深度学习的多状态融合机动目标跟踪算法
A Deep Learning-Based Multiple-State Fusion Approach for Maneuvering Target Tracking
摘要
Abstract
With the rapid development of sensor communication technologies in recent years,the ability to obtain high-precision data in real time has been greatly enhanced.This facilitates the development of data-driven approaches for solving maneuvering target tracking problems,thereby overcoming the challenges of traditional model-based filters in dealing with poor a-priori model information and high-speed target maneuvering.In particular,deep learning approaches estimate the target state by constructing end-to-end neural networks,which eliminate the reliance on a-priori information at the expense of reliance on training data and lack of physical interpretability.To solve these deficiencies,we combine the advantages of the model-driven interactive multiple model(IMM)algorithm and data-driven long short-term memory(LSTM)network,to propose a deep learning-based multiple-state fusion approach for target track maneuvering.This approach comprises multiple LSTM-based trackers and a single LSTM-based classifier.Each tracker is assigned a separate target motion state,and the trackers are executed in parallel to obtain state estimates.A classifier is used to determine the weight for each motion/tracker,and the final estimate is the weighted average of the estimates of all trackers.Simulation results demonstrated that the proposed algorithm outperforms both the IMM and end-to-end LSTM network-based tracking algorithms in terms of tracking accuracy.关键词
机动目标跟踪/深度学习/长短时记忆网络/交互多模型Key words
maneuver target tracking/deep learning/long short-term memory network/interactive multiple model分类
信息技术与安全科学引用本文复制引用
阳逸程,王静远,李天成..一种基于深度学习的多状态融合机动目标跟踪算法[J].信号处理,2025,41(7):1190-1205,16.基金项目
国家自然科学基金(62071389,62422117)The National Natural Science Foundation of China(62071389,62422117) （62071389,62422117）
