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All issues Volume 210 (2018) MATEC Web Conf., 210 (2018) 05002 References
Open Access
Issue
MATEC Web Conf.
Volume 210, 2018
22nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
Article Number 05002
Number of page(s) 6
Section Signal Processing
DOI https://doi.org/10.1051/matecconf/201821005002
Published online 05 October 2018
  1. Y. Bar-Shalom, X. R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation, (John Wiley & Sons, Hoboken, NJ, 2001). [CrossRef] [Google Scholar]
  2. F. Zhao, L. J. Guibas, Wireless Sensor Networks, (Elsevier:Amsterdam, 2004). [Google Scholar]
  3. M. Malek-Zavarei, M. Jamshidi, Time-Delay Systems:Analysis, Optimation and Application, (North-Holland: Amsterdam, The Netherlands, 1987). [Google Scholar]
  4. Y. Bar-Shalom, Update with out-of-sequence measurements in tracking: exact solution, IEEE Trans. Aerosp. Electron. Syst., 38, 769-778, (2002). [CrossRef] [Google Scholar]
  5. A. Ray, Introduction to networking for integrated control systems, IEEE Contr. Syst. Mag., 9, 76-79, (1989). [CrossRef] [Google Scholar]
  6. M. Sahebsara, T. Chen, S. L. Shah, Optimal H1 filtering with random sensor delay, multiple packet dropout and uncertain observations, IEEE Trans. Autom. Contr., 52, 1508-1513, (2007). [CrossRef] [Google Scholar]
  7. H. Zhang, G. Feng, G. Duan, X. Lu, H1 filtering for multiple-time- delay measurements IEEE Trans. Signal Process., 54, 1681-1688, (2006). [CrossRef] [Google Scholar]
  8. M. Chitre, S. Shahabudeen, M. Stojanovic, Underwater acoustic communications and networking: recent advances and future challenges,Marine Technology Society J., 42, 103-116, (2008). [CrossRef] [Google Scholar]
  9. Z. Wang, D. W. C. Ho, X. Liu, Robust filtering under randomly varying sensor delay with variance constraints, IEEE Trans. Circuits Systems-II: Express Briefs, 51, 320-326, (2004). [CrossRef] [Google Scholar]
  10. M. Liu, H. Chen, H1 state estimation for discretetime delayed systems of the neural network type with multiple missing measurements IEEE Trans. Signla Process., 26, 2987-2998, (2015). [Google Scholar]
  11. Y. S. Shmaliy, A. V. Marienko, A. V. Savchuk, GPSBased Optimal Kalman Estimation of Time Error Frequency Offset, and Aging, Proc. 31st PTTI Meeting, Dana Point, California, USA, 431-440, (1999). [Google Scholar]
  12. A. H. Jazwinski, Stochastic Processes and Filtering Theory, (New York: Academic Press, 1970). [Google Scholar]
  13. Y. S. Shmaliy, S. Zhao, C. K. Ahn, Unbiased FIR filtering: an iterative alternative to Kalman filtering ignoring noise and initial conditions, IEEE Contr. Syst. Mag., 37, 70-89, (2017). [Google Scholar]
  14. Y. S. Shmaliy, Unbiased FIR filtering of discretetime polynomial statespace models, IEEE Trans. Signal Process., 57, 1241-1249, (2009). [CrossRef] [Google Scholar]
  15. Y. S. Shmaliy, An iterative Kalman-like algorithmignoring noise and initial conditions, IEEE Trans. Signal Process., 59, 2465-2473, (2011). [CrossRef] [Google Scholar]
  16. F. Ramirez-Echeverria, A. Sarr, Y. S. Shmaliy, Optimal memory of discrete-time FIR filters in state-space, IEEE Trans. Signal Process., 62, 557-561, (2014). [Google Scholar]
  17. S. Zhao, Y. S. Shmaliy, F. Liu, Fast Kalman-like optimal unbiased FIR filtering with applications, IEEE Trans. on Signal Process., 64, 2284-2297, (2016). [Google Scholar]
  18. S. Zhao, Y. S. Shmaliy, F. Liu, Fast computation of discrete Optimal FIR estimates in white Gaussian noise, IEEE Signal Process. Lett., 22, 718-722, (2015). [CrossRef] [Google Scholar]
  19. W. H. Kwon, S. Han, Receding Horizon Control:Model Predictive Control for State Models, (London:Springer, 2005). [Google Scholar]
  20. Z. Quan, A. Han, W. H. Kwon. A robust FIR Filter for linear discrete-time state-space signal models with uncertainties, IEEE Signal Process. Lett., 14, 553-556, (2007). [CrossRef] [Google Scholar]
  21. C. K. Ahn, Strictly passive FIR filtering for statespacemodelswith external disturbance, Int. J. Electron. Commun., 66, 944-948, (2012). [CrossRef] [Google Scholar]
  22. C K. Ahn, P. Shi, L. Wu, Receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay, IEEE Trans. Cybernetics, 45, 2680-2692, (2015). [CrossRef] [Google Scholar]
  23. I. Y. Song, D. Y. Kim, V. Shin, M. Jeon, Receding horizon filtering for discrete-time linear systems with state and observation delays, IET Radar, Sonar & Navigation, 6, 263-271, (2012). [CrossRef] [Google Scholar]
  24. I. Y. Song, V. Shin, M. Jeon, Distributed fusion receding horizon filtering for uncertain linear stochastic systems with time-delay sensors, J. Franklin Inst., 349, 928-946, (2012). [CrossRef] [Google Scholar]
  25. H. D. Choi, C. K. Ahn, M. T. Lim, M. K. Song, Dynamic output-feedback H1 control for active halfvehicle suspension systems with time-varying input delay, Int. J. Contr., Autom., Syst., 14, 59-68, (2016). [CrossRef] [Google Scholar]
  26. Y. S. Shmaliy, S. Zhao, C. K. Ahn, Unbiased FIR filtering:an iterative alternative to Kalman filtering ignoring noise and initial conditions, IEEE Control Systems Mag., 37 (5) 70-89, (2017). [Google Scholar]
  27. Y. S. Shmaliy, D. Simon, Iterative unbiased FIR state estimation: a review of algorithms, EURASIP J. Advances Signal Process., 113, 1-16, (2013). [Google Scholar]
  28. D. Simon, Optimal Estimation: Kalman, H1, and Nonlinear Approaches, (Wiley, Hoboken, NJ, 2006). [Google Scholar]

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