Multi-level Deep Learning Kalman Filter

Shi Yan, Yan Liang, Binglu Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

The well-known Kalman filter and its adaptive variants belong to model-based optimization, and their optimality depends on reliable prior information such as system models, which is sometimes hard to obtain. To reasonably introduce prior domain knowledge on the basis of offline data learning, a multi-level deep learning Kalman filter is designed in this paper with dynamic model parameter learning for evolution trend prediction, process noise covariance learning to obtain the optimal gain, and compensation term learning to correct the errors after the filtering update. The gated recurrent unit is used to construct offline learning modules, which endow the multi-level filter with nonlinear model fitting and memory iterative learning capabilities. The proposed algorithm is validated in maneuvering target tracking tasks, showcasing significant enhancements.

源语言英语
主期刊名2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
出版商Institute of Electrical and Electronics Engineers Inc.
1113-1118
页数6
ISBN(电子版)9798350300178
DOI
出版状态已出版 - 2023
活动8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023 - Sanya, 中国
期限: 8 7月 202310 7月 2023

出版系列

姓名2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

会议

会议8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
国家/地区中国
Sanya
时期8/07/2310/07/23

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