Kalman Filtering with Intermittent Observations: Bounds on the Error Covariance Distribution

EDUARDO ROHR; DAMIÁN MARELLI; MINYUE FU

Orlando

Conferencia; IEEE Conference on Decision and Control (CDC); 2011

Institute of Electrical and Electronics Engineers (IEEE)

When measurements are subject to random losses,the covariance of the estimation error of a state estimatorbecomes a random variable. In this paper we present boundson the cumulative distribution function of the covariance ofthe estimation error for a discrete time linear system. Wealso show that the bounds can be arbitrarily tight if sufficientcomputational power is available. Numerical simulations showthat the proposed method provides tighter bounds than the onesavailable in the literature.