An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning

作     者：ZHU Hongfeng XIONG Wei CUI Yaqi ZHU Hongfeng;XIONG Wei;CUI Yaqi

作者机构：Institute of Information Fusion Naval Aviation University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第5期

页      面：1120-1132页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Major Program of the National Natural Science Foundation of China (61790554) the Youth Program of National Natural Science Foundation of China (62001499) 

主　　题：Maneuvering target tracking Neural networks End-to-end learning Interactive multiple-model Model transition probability matrix 

摘      要：The interactive multiple-model(IMM) is a popular choice for target tracking. However, to design transition probability matrices(TPMs) for IMMs is a considerable challenge with less prior knowledge, and the TPM is one of the fundamental factors influencing IMM performance. IMMs with inaccurate TPMs can make it difficult to monitor target maneuvers and bring poor tracking results. To address this challenge, we propose an adaptive IMM algorithm based on end-to-end learning. In our method, the neural network is utilized to estimate TPMs in real-time based on partial parameters of IMM in each time step, resulting in a generalized recurrent neural network. Through end-to-end learning in the tracking task, the dataset cost of the proposed algorithm is smaller and the generalizability is stronger. Simulation and automatic dependent surveillance-broadcast tracking experiment results show that the proposed algorithm has better tracking accuracy and robustness with less prior knowledge.