Quadratic estimation for discrete time-varying non-Gaussian systems with multiplicative noises and quantization effects. (March 2020)

• Record Type: Journal Article
• Authors: Liu, Qinyuan; Wang, Zidong; Han, Qing-Long; Jiang, Changjun
• Is Part Of: Automatica. Volume 113(2020)
• Publication Date: 2020-03
• Subjects: Remote state estimation -- Kalman filter -- Quantization effects -- Recursive difference equations -- Non-Gaussian noises; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.108714
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper is concerned with the remote state estimation problem for a class of linear discrete time-varying non-Gaussian systems with multiplicative noises. Due to bandwidth constraints in digital communication networks, the measured outputs are quantized before transmission by a probabilistic uniform quantizer. Our attention is focused on the design of a recursive quadratic estimator that exploits the quadratic functions of the measurements. By introducing a proper augmented system which aggregates the original state vector and its second-order Kronecker power, we are able to transfer the quadratic estimation problem into a corresponding linear estimation problem of the augmented state vector. An upper bound is first established for the covariance of the estimation error that is expressed in terms of the solutions to certain matrix difference equations, and such an upper bound is then minimized by designing the filter parameters in an iterative manner. Subsequently, we discuss the monotonicity of the optimized upper bound with respect to the quantization accuracy. A numerical example is provided to verify the effectiveness of the proposed filtering algorithm.