A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 12
Dec.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
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

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

doi: 10.1109/JAS.2021.1004144
More Information
  • 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.

     

  • loading
  • [1]
    J. W. Song and C. C. Park, “Enhanced pedestrian navigation based on course angle error estimation using cascaded Kalman filters,” Sensors, vol. 18, no. 4, Apr. 2018. doi: 10.3390/s18041281
    [2]
    L. Van Nguyen and H. M. La, “Real-time human foot motion localization algorithm with dynamic speed,” IEEE Trans. Human-Mach. Syst., vol. 46, no. 6, pp. 822–833, Dec. 2016. doi: 10.1109/THMS.2016.2586741
    [3]
    C. Tsirmpas, A. Rompas, O. Fokou, and D. Koutsouris, “An indoor navigation system for visually impaired and elderly people based on radio frequency identification (RFID),” Inform. Sci., vol. 320, pp. 288–305, Nov. 2015. doi: 10.1016/j.ins.2014.08.011
    [4]
    G. Caso, L. De Nardis, F. Lemic, V. Handziski, A. Wolisz, and M. G. Di Benedetto, “ViFi: Virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model,” IEEE Trans. Mobile Comput., vol. 19, no. 6, pp. 1478–1491, Jun. 2020. doi: 10.1109/TMC.2019.2908865
    [5]
    Y. Zheng, Q. Li, C. Wang, X. Li, and Y. Huang, “Magnetic-based positioning system for moving target with feature vector,” IEEE Access, vol. 8, pp. 105472–105483, Jun. 2020. doi: 10.1109/ACCESS.2020.3000305
    [6]
    Y. X. Zheng, Q. H. Li, C. H. Wang, W. Z. Yu, and Q. Sun, “High-precision calibration method for position and attitude angle of magnetic beacon,” J. Chin. Inertial Technol., vol. 28, no. 3, pp. 353–359, Jun. 2020.
    [7]
    M. Xiang and J. Zhao, “New results on the performance of distributed Bayesian detection systems,” IEEE Trans. Syst. Man Cybern. Part A Syst. Humans, vol. 31, no. 1, pp. 73–78, Jan. 2001. doi: 10.1109/3468.903869
    [8]
    Y. Zheng, Q. Li, X. Wang, L. Wu, and X. Li, “Advanced positioning system for harsh environments using time-varying magnetic field,” IEEE Trans. Magn., vol. 57, no. 6, Jun. 2021. doi: 10.1109/TMAG.2020.3041389
    [9]
    S. Pequito, S. Kar, and A. P. Aguiar, “A structured systems approach for optimal actuator-sensor placement in linear time-invariant systems,” in Proc. American Control Conf., Washington, USA, 2013, pp. 6108−6113.
    [10]
    T. H. Summers, F. L. Cortesi, and J. Lygeros, “On submodularity and controllability in complex dynamical networks,” IEEE Trans. Control Netw. Syst., vol. 3, no. 1, pp. 91–101, Mar. 2016. doi: 10.1109/TCNS.2015.2453711
    [11]
    M. A. Belabbas, “Geometric methods for optimal sensor design,” Proc. Royal Soc., vol. 472, no. 2185, p. 20150312, Jan. 2016.
    [12]
    S. P. Chepuri and G. Leus, “Sparsity-promoting sensor selection for non-linear measurement models,” IEEE Trans. Signal Process., vol. 63, no. 3, pp. 684–698, Feb. 2015. doi: 10.1109/TSP.2014.2379662
    [13]
    A. Nordio, A. Tarable, F. Dabbene, and R. Tempo, “Sensor selection and precoding strategies for wireless sensor networks,” IEEE Trans. Signal Process., vol. 63, no. 16, pp. 4411–4421, Aug. 2015. doi: 10.1109/TSP.2015.2439239
    [14]
    V. Tzoumas, A. Jadbabaie, and G. J. Pappas, “Sensor placement for optimal Kalman filtering: Fundamental limits, submodularity, and algorithms,” in Proc. American Control Conf., Boston, USA, 2016, pp. 191−196.
    [15]
    E. W. Bai, H. E. Baidoo-Williams, and R. Mudumbai, “Robust tracking of piecewise linear trajectories with binary sensor networks,” Automatica, vol. 61, pp. 134–145, Nov. 2015. doi: 10.1016/j.automatica.2015.07.012
    [16]
    S. T. Jawaid and S. L. Smith, “Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems,” Automatica, vol. 61, pp. 282–288, Nov. 2015. doi: 10.1016/j.automatica.2015.08.022
    [17]
    C. Li and N. Elia, “Stochastic sensor scheduling via distributed convex optimization,” Automatica, vol. 58, pp. 173–182, Aug. 2015. doi: 10.1016/j.automatica.2015.05.014
    [18]
    D. Han, J. F. Wu, H. S. Zhang, and L. Shi, “Optimal sensor scheduling for multiple linear dynamical systems,” Automatica, vol. 75, pp. 260–270, Jan. 2017. doi: 10.1016/j.automatica.2016.09.015
    [19]
    J. K. Hu, “Research on the autonomous pedestrian navigation system based on MIMU,” M.S. thesis, Harbin Institute of Technology, Harbin, China, 2019, pp. 35−50.
    [20]
    F. Zhang and Q. Zhang, “Eigenvalue inequalities for matrix product,” IEEE Trans. Automat. Control, vol. 51, no. 9, pp. 1506–1509, Sept. 2006. doi: 10.1109/TAC.2006.880787
    [21]
    W. L. Zhao, “Research on multi-source fusion positioning theory and method,” Ph.D. dissertation, Harbin Institute of Technology, Harbin, China, 2018, pp. 65−86.
    [22]
    Y. B. Zou, “Research on TOA/TDOA localization using convex optimization methods,” Ph.D. dissertation, Univ. Elect. Science and Technology of China, Chengdu, China, 2013, pp. 54−76.
    [23]
    Y. X. Zheng, Q. H. Li, C. H. Wang, X. N. Li, and B. S. Yang, “A magnetic based indoor positioning method on fingerprint and confidence evaluation,” IEEE Sensors J., vol. 21, no. 5, pp. 5932–5943, Mar. 2021. doi: 10.1109/JSEN.2020.3038390

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(18)  / Tables(1)

    Article Metrics

    Article views (905) PDF downloads(51) Cited by()

    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

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return