Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Yuanxun Zheng; Qinghua Li; Changhong Wang; Xiaoguang Wang; Lifeng Hu

doi:10.1109/JAS.2021.1004144

Volume 9 Issue 12

Dec. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(12): 2174-2185

Y. X. Zheng, Q. H. Li, C. H. Wang, X. G. Wang, and L. F. Hu, “Multi-source adaptive selection and fusion for pedestrian dead reckoning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2174–2185, Dec. 2022. doi: 10.1109/JAS.2021.1004144

Citation:

Y. X. Zheng, Q. H. Li, C. H. Wang, X. G. Wang, and L. F. Hu, “Multi-source adaptive selection and fusion for pedestrian dead reckoning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2174–2185, Dec. 2022. doi: 10.1109/JAS.2021.1004144

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Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

doi: 10.1109/JAS.2021.1004144

1.
NORINCO GROUP Air Ammunition Research Institute, Harbin, and also with the Research Center of Space Control and Inertial Technology, Harbin Institute of Technology, Harbin 150001, China
2.
Research Center of Space Control and Inertial Technology, Harbin Institute of Technology, Harbin 150001, China
3.
NORINCO GROUP Air Ammunition Research Institute, Harbin 150030, China

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Author Bio:
Yuanxun Zheng received the M.S. degree and Ph.D. degree from Research Center of Space Control and Inertial Technology, Harbin Institute of Technology in 2014 and 2021, respectively. He works in NORINCO GROUP Air Ammunition Research Institute as Engineer now. His research interests include magnetic based navigation, signal of opportunity navigation, multi-source information fusion and optimal information selection

Qinghua Li received the B.S., M.S and Ph.D. degrees in control science and engineering from Harbin Institute of Technology in 2001, 2003, and 2008, respectively. From 2011 to 2012, he was a Research Assistant with Department of Aerospace Engineering, University of Michigan. Since 2006, he has worked at Research Center of Space Control and Inertial Technology, Harbin Institute of Technology. His current research interests include magnetic information navigation, inertial navigation, and motion control

Changhong Wang received the B.E., M.S., and Ph.D. degrees in control science and engineering from the Harbin Institute of Technology, in 1983, 1986, and 1991, respectively. From 1993 to 1994, he was a Post-Doctoral Fellow in the Automation Department, Faculté Polytechnique de Mons, Belgium. In 1986, he joined the Department of Control Science and Control Engineering, Harbin Institute of Technology, where he is currently a Full Professor. He has served as the Director of Control Theory and Application for five years, and as the Chairman of the Department of Control Science and Control Engineering for three years. He is currently the Deputy Dean of the Academy of Industrial Technology at Harbin Institute of Technology. His research interests include intelligent systems, intelligent control, and network control

Xiaoguang Wang received the Ph.D. degree from Institute of Mechanical and Electrical Engineering, Beijing Institute of Technology in 2016. He works in NORINCO GROUP Air Ammunition Research Institute as Senior Engineer. His research interests include system engineering and aircraft overall technology

Lifeng Hu received the M. S. degree from Harbin Institute of Technology in 2020. He works in NORINCO GROUP Air Ammunition Research Institute and is rated as the Young Technology Leaders of NORINCO GROUP. His research interests include system engineering and aircraft overall technology
Corresponding author: Yuanxun Zheng, e-mail: 252988642@qq.com
Received Date: 2020-12-01
Revised Date: 2021-02-19
Accepted Date: 2021-03-14

Available Online: 2021-04-01

Abstract

Abstract

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.
- Adaptive reliability evaluation,
- adaptive weight evaluation,
- Kalman filter (KF),
- multi-source fusion,
- optimal set selection

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References

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Information sources with high confidence can be selected adaptively
Only high-confidence information sources participate in information fusion and PDR, with higher accuracy
The method with a simple structure may be widely applied in engineering

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

doi: 10.1109/JAS.2021.1004144

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