Linear minimum variance estimators for systems with bounded random measurement delays and packet dropouts

Authors:
Shuli Sun
Affiliations:
Department of Automation, Heilongjiang University, Harbin 150080, PR China
Venue:
Signal Processing
Year:
2009

Citing 9
Cited 6

Stochastic analysis and control of real-time systems with random time delays

Automatica (Journal of IFAC)
Linear recursive discrete-time estimators using covariance information under uncertain observations

Signal Processing - From signal processing theory to implementation
Minimum variance generalized state estimators for multiple sensors with different delay rates

Signal Processing
Brief paper: Optimal linear estimation for systems with multiple packet dropouts

Automatica (Journal of IFAC)
Control and Estimation of Systems with Input/Output Delays

Control and Estimation of Systems with Input/Output Delays
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements

IEEE Transactions on Signal Processing
H∞ filtering for multiple-time-delay measurements

IEEE Transactions on Signal Processing
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters

Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation

IEEE Transactions on Information Theory

Decentralized robust set-valued state estimation in networked multiple sensor systems

Computers & Mathematics with Applications
Minimum variance filter with packet dropouts in wireless sensor networks

ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Improved fractional Kalman filter and its application to estimation over lossy networks

Signal Processing
Recursive least-squares quadratic smoothing from measurements with packet dropouts

Signal Processing
Least-squares linear estimators using measurements transmitted by different sensors with packet dropouts

Digital Signal Processing
Linear estimation for networked control systems with random transmission delays and packet dropouts

Information Sciences: an International Journal

Quantified Score

Hi-index	0.08

Visualization

Abstract

For discrete-time stochastic linear systems with bounded random measurement delays and packet dropouts, the optimal estimators including filter, predictor and smoother are developed in the linear minimum variance sense based on the innovation analysis approach. Some binary distributed random variables with known distributions are employed to describe the phenomenon of random delays and packet dropouts. Compared with the augmented approach, the computational cost is reduced. Furthermore, the proposed algorithm also gives a suboptimal estimate for systems with unbounded delays and packet dropouts by selecting a sufficient large upper bound. A simulation shows the effectiveness of the proposed algorithms.