Deconvolution Estimation of Systems with Packet Dropouts
This paper studies the optimal and suboptimal deconvolution problems over a network subject to random packet losses,which are modeled by an independent identically distributed Bernoulli process.By the projection formula,an optimal input white noise estimator is first presented with a stochastic Kalman filter.We show that this obtained deconvolution estimator is time-varying,stochastic,and it does not converge to a steady value.Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria.The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator.It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.
Networked system Packet dropout White noise estimation Convergence analysis
Chunyan Han Wei Wang
School of Electrical Engineering, University of Jinan, Jinan 250022 School of Control Science and Engineering, Shandong University, Jinan 250061
国际会议
the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)
贵阳
英文
4588-4593
2013-05-01（万方平台首次上网日期，不代表论文的发表时间）