A Robust Finite-Horizon Kalman Filter for Uncertain Discrete Time-Varying Systems with State-Delay and Missing Measurements

Zheng, Jun-Hui; Liu, Jian-Fen

doi:10.14257/ijgdc.2016.9.3.24

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are established in terms of two discrete Riccati difference equations. In [16], a robust Kalman filter is designed for the uncertain time-varying discrete-time stochastic systems with state delay and missing measurement characterized by the Bernoulli random variables. In [17] and [18], the regularized robust filters are proposed in linear discrete-time stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

Robust RLS Wiener Signal Estimators for Discrete-Time Stochastic Systems with Uncertain Parameters

Nakamori¹

2019

FSP

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This paper proposes the robust recursive least-squares (RLS) Wiener fixed-point smoother and filter in linear discrete-time stochastic systems with parameter uncertainties. The uncertain parameters exist in the observation matrix and the system matrix. The uncertain parameters cause to generate the degraded signal. In this paper, the degraded signal process is fitted to the finite order autoregressive (AR) model. The robust RLS Wiener estimators use the system matrices and the observation matrices for both the signal and the degraded signal, the variance of the state vector for the degraded signal, the cross-variance of the state vector for the signal with the state vector for the degraded signal, and the variance of the white observation noise. Also, this paper proposes the robust recursive fixed-point smoother and filter, by using the covariance information of the state vector for the degraded signal, the cross-covariance information of the state vector for the signal with the state vector for the degraded signal, the observation matrices for both the degraded signal and the signal besides the variance of the white observation noise. In estimating the signal process expressed by the second order AR model, the proposed robust RLS Wiener filter is superior in estimation accuracy to the robust Kalman filter and the RLS Wiener filter.

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Section: Introductionmentioning

confidence: 99%

Robust RLS Wiener Signal Estimators for Discrete-Time Stochastic Systems with Uncertain Parameters

Nakamori¹

2019

FSP

View full text Add to dashboard Cite

show abstract

A Robust Finite-Horizon Kalman Filter for Uncertain Discrete Time-Varying Systems with State-Delay and Missing Measurements

Cited by 1 publication

References 17 publications

Robust RLS Wiener Signal Estimators for Discrete-Time Stochastic Systems with Uncertain Parameters

Robust RLS Wiener Signal Estimators for Discrete-Time Stochastic Systems with Uncertain Parameters

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