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ZHU Hongfeng, XIONG Wei, CUI Yaqi. An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.442
Citation: ZHU Hongfeng, XIONG Wei, CUI Yaqi. An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.442

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

doi: 10.1049/cje.2021.00.442
Funds:  This work was supported by the Major Program of the National Natural Science Foundation of China (61790554) and Youth Program of National Natural Science Foundation of China (62001499)
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  • Author Bio:

    (corresponding author) was born in 1994. He received the M.S. degree from Naval Aviation University in 2018, and is currently pursuing the Ph.D. degree in information and communication engineering at Naval Aviation University. His research interests include radar data processing, and target tracking with artificial intelligence. (Email: 1181630959@qq.com)

    was born in 1977. He received the Ph.D. degree from Naval Aviation University in 2005. He is currently a Full Professor at the Naval Aviation University. He is the Member and Director General of Information Fusion Branch of Chinese Society of Aeronautics and Astronautics. His research interests include radar data processing, multi-sensor information fusion and machine learning. (Email: xiongwei@csif.org.cn)

    was born in 1987. He received the Ph.D. degree in information and communication engineering from Naval Aviation University in 2014. He is an Associate Professor at Naval Aviation University. His research interests include information fusion, machine learning, and deep learning with their applications in information fusion. (Email: cui_yaqi@126.com)

  • Received Date: 2021-12-22
  • Accepted Date: 2022-07-03
  • Available Online: 2022-07-19
  • 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 (ADS-B) tracking experiment results show that the proposed algorithm has better tracking accuracy and robustness with less prior knowledge.
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