e2af4404-01a7-4661-909b-50f253048e2920220802111030356wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS2224-266X1109-273410.37394/23201http://wseas.org/wseas/cms.action?id=28612720222720222110.37394/23201.2022.21https://wseas.com/journals/cas/2022.phpKarenUribe-MurciaDepartment of Electronics Engineering University of Guanajuato, Salamanca, 36885, MEXICOJorge A.Ortega-ContrerasDepartment of Electronics Engineering University of Guanajuato, Salamanca, 36885, MEXICOEli G.Pale-RamonDepartment of Electronics Engineering University of Guanajuato, Salamanca, 36885, MEXICOMiguelVazquez-OlguinDepartment of Electronics Engineering University of Guanajuato, Salamanca, 36885, MEXICOYuriy S.ShmaliyDepartment of Electronics Engineering University of Guanajuato, Salamanca, 36885, MEXICOIn this article, a comparison of the UFIR and Kalman filter to estimate a tracking vehicle system variables is developed considering two possible observation output models. The time stamp approach and the predictive compensation are used to analyze the problem from multiple perturbations, which produces random delayed data and losses during transmissions. For the estimation, a transformation model and a decorrelation covariance matrices are developed with the aim of assure optimal conditions and minimizing the estimation error. Finally, several real situations, miss modeling, uncertain noise covariances, and uncertain probabilities are proposed to demonstrate the effectiveness and robustness of the filter proposed.82202282202217618119https://wseas.com/journals/cas/2022/a385101-015(2022).pdf10.37394/23201.2022.21.19https://wseas.com/journals/cas/2022/a385101-015(2022).pdf10.1016/j.jestch.2018.03.009P. Mishra, V. Kumar and K Rana, An efficient method for parameter estimation of a nonlinear system using Backtracking Search Algorithm,Int. J. Eng. Sci. Techn., vol. 21, no. 3, pp. 338-350, 2018 10.1016/j.measurement.2021.109542A. Mehrjouyan, M. Menhaj and M. Khosravi, Robust observer-based adaptive synchronization control of uncertain nonlinear bilateral teleoperation systems under time-varying delay,Measurement, vol. 182, pp. 109542, 2021 10.1016/j.jestch.2020.05.001O. Alamu, A. Gbenga-Ilori, M. Adelabu, A. Imoize and O. Ladipo, Energy efficiency techniques in ultra-dense wireless heterogeneous networks: An overview and outlook,Int. J. Eng. Sci. Techn., vol. 23, pp. 1308-1326, 2020 10.1016/j.jestch.2015.12.010A. Illa, N. Haridas and E. Elias Design of multiplier-less FIR filters with simultaneously variable bandwidth and fractional delay,Int. J. Eng. Sci. Techn., vol. 19, pp. 1160-1165, 2016 10.1016/j.sigpro.2016.02.014R. Caballero-Aguila, A. Hermoso-Carazo and J. Linares-P ´ erez, ´ Fusion estimation using measured outputs with random parameter matrices subject to random delays and packet dropouts,Signal Process., vol. 127, pp. 12-23, 2016 10.1016/j.sigpro.2018.01.015F. Han, H. Dong, Z. Wang, G. Li, and F. Alsaadi, Improved Tobit Kalman filtering for systems with random parameters via conditional expectation, Signal Processing, vol. 147, pp. 35-45, 2018 10.1080/00207721.2017.1289568R. Caballero-Aguila, A. Hermoso-Carazo, and J. Linares-P ´ erez, ´ New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts, International Journal of Systems Science, vol. 48, no. 9, pp. 1805–1817, 2017 T. Lee, J. Park and H. Jung, Network-based H state estimation for neural networks using imperfect measurement, Applied Mathematics and Computation, vol. 316, pp. 205–214, 2018 10.1016/j.neucom.2021.01.127K. Uribe-Murcia, Y. Shmaliy and J. Andrade-Lucio, Unbiased FIR, Kalman, and game theory H filtering under bernoulli distributed random delays and packet dropouts, Neurocomputing, vol. 442, pp. 89–97, 2021 10.1016/j.inffus.2018.01.004J. Ma, and S. Sun, A general packet dropout compensation framework for optimal prior filter of networked multi-sensor systems, Information Fusion, vol. 45, pp. 128–137, 2019 10.1049/iet-spr.2012.0348C. Zhu, Y. Xia, L. Xie, and L. Yan, Optimal linear estimation for systems with transmission delays and packet dropouts, IET signal Processing, vol. 7, no. 7, pp. 814–823, 2013 10.1016/j.sigpro.2018.10.012R. Caballero-Aguila and A. Hermoso-Carazo and J. Linares-P ´ erez, ´ Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing, Signal Processing, vol. 156,pp. 71–83, 2019. 10.1109/tac.2012.2229812S. Sun, Optimal linear filters for discrete-time systems with randomly delayed and lost measurements with/without time stamps, IEEE Transactions on Automatic Control, vol. 58, no. 6, pp. 1551–1556, 2012 10.1109/access.2020.3014209J. Ma, and S. Sun, Optimal Linear Filter for Systems With Random Delay and Packet Dropout Compensations, IEEE Access, vol. 8, pp. 145268–145277, 2020 10.1109/access.2020.2966521J. Feng, R. Yang, H. Liu and B. Xu, Robust recursive estimation for uncertain systems with delayed measurements and noises, IEEE Access, vol. 8, pp. 14386–14400, 2020 10.1186/1687-6180-2013-113Y. S. Shmaliy and D. Simon, “Iterative unbiased FIR state estimation: a review of algorithms,” EURASIP J. Advances Signal Process., vol. 113, no. 1, pp. 1–16, 2013. 10.1109/mcs.2017.2718830Y. 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 Syst. Mag., vol. 37, no. 5, pp. 70–89, 2017. 10.1109/tac.2019.2937850K. Uribe-Murcia, Y. S. Shmaliy, C. K. Ahn, and S. Zhao, Unbiased FIR Filtering for time-stamped discretely delayed and missing data, IEEE Trans. Autom. Control, 2020 (to be published). 10.1201/9781351012355S. Bhaumik and P. Date, Paresh, Nonlinear estimation: methods and applications with deterministic Sample Points, CRC Press, 2019. T. Kailath, A. H. Sayed and B. Hassibi, Linear Estimation, Prentice Hall, 2000 Geolife GPS trajectory dataset - User Guide, 2011. Available: https://www.microsoft.com/en-us/research/publication/geolife-gpstrajectory-dataset-user-guide. D. Simon, Optimal Estimation: Kalman, H∞, and Nonlinear Approaches, J. Wiley & Sons, Hoboken, NMJ, 2006.