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Volume 9 Issue 12
Dec.  2022

IEEE/CAA Journal of Automatica Sinica

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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
shu

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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    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.

     

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    Highlights

    • 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

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