Open Access
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
Article Number 04003
Number of page(s) 6
Section Signal Processing
Published online 24 September 2019
  1. D. J. Cook, J. C. Augusto, and V. R. Jakkula, “Ambient intelligence: Technologies, applications, and opportunities,” Pervasive and Mobile Computing, vol. 5, no. 4, pp. 277–298, aug 2009. [CrossRef] [Google Scholar]
  2. L. G. Feng Zhao, Wireless Sensor Networks: An Information Processing Approach. MORGAN KAUF-MANN PUBL INC, 2004. [Google Scholar]
  3. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393–422, Mar 2002. [CrossRef] [Google Scholar]
  4. M. S. Mahmoud and Y. Xia, Networked Filtering and Fusion in Wireless Sensor Networks. CRC Press, 2014. [Google Scholar]
  5. C. Chen, S. Zhu, X. Guan, and X. Shen, Wireless Sensor Networks. Springer Intern. Publ., 2014. [Google Scholar]
  6. B. Rao and H. Durrant-Whyte, “Fully decentralised algorithm for multisensor Kalman filtering,” IEE Proc. D Control Theory and Applic., vol. 138, no. 5, p. 413, 1991. [CrossRef] [Google Scholar]
  7. H. Dong, Z. Wang, and H. Gao, “Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet ropouts,” IEEE Trans. Signal Process., vol. 60, no. 6, pp. 3164–3173, Jun 2012. [CrossRef] [Google Scholar]
  8. I. D. Schizas, A. Ribeiro, and G. B. Giannakis, “Consensus in Ad Hoc WSNs with noisy links—Part I: Distributed estimation of deterministic signals,” IEEE Trans. Signal Process., vol. 56, no. 1, pp. 350–364, Jan 2008. [CrossRef] [Google Scholar]
  9. W. Li, Z. Wang, G. Wei, L. Ma, J. Hu, and D. Ding, “A survey on multisensor fusion and consensus filtering for sensor networks,” Discrete Dynamics in Nature and Society, Vol. 2015, pp. 1–12, 2015. [Google Scholar]
  10. R. Olfati-Saber and R. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1520–1533, sep 2004. [CrossRef] [MathSciNet] [Google Scholar]
  11. X. Bai, Z. Wang, L. Zou, and F. E. Alsaadi, “Collaborative fusion estimation over wireless sensor networks for monitoring CO 2 concentration in a greenhouse,” Information Fusion, Vol. 42, pp. 119–126, Jul 2018. [CrossRef] [Google Scholar]
  12. R. Olfati-Saber, “Distributed Kalman filtering for sensor networks,” in 2007 46th IEEE Conf. on Decision and Control. IEEE, 2007. [Google Scholar]
  13. R. Carli, A. Chiuso, L. Schenato, and S. Zampieri, “Distributed kalman filtering based on consensus strategies,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 4, pp. 622–633, 2008. [CrossRef] [Google Scholar]
  14. S. S. Stanković, M. S. Stanković, and D. M. Sti-panović, “Consensus based overlapping decentralized estimation with missing observations and communication faults,” Automatica, vol. 45, no. 6, pp. 1397–1406, jun 2009. [CrossRef] [Google Scholar]
  15. J. J. Pomarico-Franquiz and Y. S. Shmaliy, “Accurate self-localization in RFID tag information grids using FIR filtering,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1317–1326, May 2014. [CrossRef] [Google Scholar]
  16. Y. S. Shmaliy, S. Khan, and S. Zhao, “Ultimate iterative UFIR filtering algorithm,” Measurement, Vol. 92, pp. 236–242, Oct 2016. [CrossRef] [Google Scholar]
  17. Y. S. Shmaliy, “Suboptimal FIR filtering of nonlinear models in additive white gaussian noise,” IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5519–5527, oct 2012. [CrossRef] [Google Scholar]
  18. J. Contreras-Gonzalez, O. Ibarra-Manzano, and Y. S. Shmaliy, “Clock state estimation with the kalman-like UFIR algorithm via TIE measurement,” Measurement, vol. 46, no. 1, pp. 476–483, jan 2013. [CrossRef] [Google Scholar]
  19. M. Vazquez-Olguin, Y. S. Shmaliy, and O. Ibarra-Manzano, “Developing UFIR Filtering with Consensus on Estimates for Distributed Wireless Sensor Networks,” WSEAS Trans. Circuits Syst., 2018. [Google Scholar]
  20. A. H. Jazwinski, Stochastic Processes and Filtering Theory (Dover Books on Electrical Engineering). Dover Publications, 2007. [Google Scholar]
  21. F. Ramirez-Echeverria, A. Sarr, and Y. S. Shmaliy, “Optimal memory for discrete-time FIR filters in state-space,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 557–561, feb 2014. [CrossRef] [Google Scholar]
  22. S. Zhao, Y. S. Shmaliy, and F. Liu, “Fast kalman-like optimal unbiased FIR filtering with applications,” IEEE Transactions on Signal Processing, vol. 64, no. 9, pp. 2284–2297, may 2016. [CrossRef] [Google Scholar]
  23. Y. S. Shmaliy, S. H. Khan, S. Zhao, and O. Ibarra-Manzano, “General unbiased FIR filter with applications to GPS-based steering of oscillator frequency,” IEEE Transactions on Control Systems Technology, vol. 25, no. 3, pp. 1141–1148, may 2017. [CrossRef] [Google Scholar]
  24. M. Farina, G. Ferrari-Trecate, and R. Scattolini, “Distributed moving horizon estimation for linear constrained systems,” IEEE Transactions on Automatic Control, vol. 55, no. 11, pp. 2462–2475, 2010. [CrossRef] [Google Scholar]
  25. B. Shen, Z. Wang, and Y. Hung, “Distributed h-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case,” Automatica, vol. 46, no. 10, pp. 1682–1688, 2010. [CrossRef] [Google Scholar]
  26. M. Vazquez-Olguin, Y. S. Shmaliy, and O. G. Ibarra-Manzano, “Distributed unbiased FIR filtering with average consensus on measurements for WSNs,” IEEE Trans. Ind. Informat., vol. 13, no. 3, pp. 1440–1447, Jun 2017. [CrossRef] [Google Scholar]
  27. J. Hu, Z. Wang, H. Gao, and L. K. Stergioulas, “Extended kalman filtering with stochastic nonlinearities and multiple missing measurements,” Automatica, vol. 48, no. 9, pp. 2007–2015, sep 2012. [CrossRef] [Google Scholar]
  28. M. Vazquez-Olguin, Y. S. Shmaliy, C. K. Ahn, and O. G. Ibarra-Manzano, “Blind robust estimation with missing data for smart sensors using UFIR filtering,” IEEE Sensors Journal, vol. 17, no. 6, pp. 1819–1827, mar 2017. [CrossRef] [Google Scholar]
  29. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. Sastry, “Kalman filtering with intermittent observations,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1453–1464, sep 2004. [CrossRef] [MathSciNet] [Google Scholar]
  30. K. Uribe-Murcia, Y. S. Shmaliy, and J. A. Andrade-Lucio, “UFIR filtering for GPS-based tracking over WSNs with delayed and missing data,” Journal of Electrical and Computer Engineering, vol. 2018, pp. 1–9, may 2018. [CrossRef] [Google Scholar]
  31. Y. S. Shmaliy, S. Zhao, and C. K. Ahn, “Unbiased finite impluse response filtering: An iterative alternative to kalman filtering ignoring noise and initial conditions,” IEEE Control Systems, vol. 37, no. 5, pp. 70–89, 2017. [Google Scholar]
  32. D. Hanley, A. B. Faustino, S. D. Zelman, D. A. Degenhardt, and T. Bretl, “MagPIE: A dataset for indoor positioning with magnetic anomalies,” in 2017 Int. Conf. Indoor Positioning and Indoor Navigation (IPIN). IEEE, sep 2017. [Google Scholar]

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