AUTHORS: Seiichi Nakamori
Download as PDF
ABSTRACT: As an extension of the linear robust recursive least-squares Wiener fixed-point smoother and filter, this paper originally designs the robust extended recursive Wiener fixed-point smoother and filter for estimating the signal in discrete-time wide-sense stationary stochastic systems. It is a characteristic in this paper that the signal is modulated with the nonlinear mechanism. As a step to the estimation problem for the observation mechanism with the nonlinear modulation, the robust signal estimators are proposed for the observation equation with the linear amplitude modulation of the signal. The observation noise is additional white noise. The system matrix in the state equation contains uncertain parameters. The robust extended recursive Wiener estimators are derived from the Wiener-Hopf equation. In the simulation example, it is shown that the proposed robust extended recursive Wiener fixed-point smoother and filter are superior in estimation accuracy to the extended recursive Wiener estimators
KEYWORDS: Discrete-time stochastic systems, robust extended recursive Wiener estimators, covariance information, fixed-point smoother, nonlinear modulation
REFERENCES:
[
1] S. Nakamori, Design of extended recursive Wiener fixed-point smoother and filter in discrete-time stochastic systems, Digital Signal Processing, Vol.17, No.1, 2007, pp. 360–370.
[2] H. Fang, N. Tian, Y. Wang, M. Zhou and M. A. Haile, Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon, IEEE/CAA Journal of Automatica Sinica, Vol.4, No.2, 2018, pp. 401–417.
[3] M. Granados-Cruz, Y. S. Shmaliy, S. H. Khan, C. K. Ahn, S. Zhao, New results in nonlinear state estimation using extended unbiased FIR filtering, 23ௗ European Signal Processing Conference, 2015, pp. 684–688.
[4] M. Fu, C. E. de Souza and Zhi-Quan Luo, Finite horizon robust Kalman filter design, IEEE Trans. Signal Processing, Vol.49, No.9, 2001, pp. 2103–2112.
[5] S. Nakamori, Robust RLS Wiener signal estimators for discrete-time stochastic systems with uncertain parameters, Frontiers in Signal Processing, Vol.3, No.1, 2019, pp. 1–18.
[6] S. Nakamori, Robust RLS Wiener FIR filter for signal estimation in linear discrete-time stochastic systems with uncertain parameters, Frontiers in Signal Processing, Vol.3, No.2, 2019, pp. 19–36.
[7] K. Xiong, C. L. Wei and L. D. Liu, Robust Kalman filtering for discrete-time nonlinear systems with parameter uncertainties, Aerospace Science and Technology, Vol.18, No.1, 2012, pp. 15–24.
[8] R. F. Souto, J. Y. Ishihara, G. A. Borges, A robust extended Kalman filter for discrete-time systems with uncertain dynamics, measurements and correlated noise, 2009 American Control Conference Hyatt Regency Riverfront, 2009, pp. 1888–1893.
[9] M. Netto, J. Zhao and L. Mili1, A robust extended Kalman filter for power system dynamic state estimation using PMU measurements, 2016 PES General Meeting, 2016, pp. 17-21.
[10] R. E. Blahut, Digital Transmission of Information, Addison–Wesley, 1990.
[11] A. P. Sage and J. L. Melsa, Estimation Theory with Applications to Communications and Control, McGraw-Hill, 1971.