A Gaussian Mixture Unscented Rauch–Tung–Striebel Smoothing Framework for Trajectory Reconstruction-Reference-Cited by-同舟云学术

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A Gaussian Mixture Unscented Rauch–Tung–Striebel Smoothing Framework for Trajectory Reconstruction

Published:2024-05 Issue:5 Volume:20 Page:7481-7491
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

He Jiacheng¹^ORCID,Peng Bei¹^ORCID,Feng Zhenyu¹^ORCID,Zhong Shan¹^ORCID,He Bo²^ORCID,Wang Gang³^ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. Ocean University of China, Qingdao, Shandong, China

3. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Major

Sichuan Science and Technology

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10521545/10438924.pdf?arnumber=10438924

Reference31 articles.

1. Model-based trajectory reconstruction with IMM smoothing and segmentation

2. A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization

3. Joint Waveform and Beamforming Design for RIS-Aided ISAC Systems

4. Improved kinematic interpolation for AIS trajectory reconstruction

5. Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol

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