Stochastic stability of extended filtering for nonlinear systems with measurement packet losses

This paper is concerned with stochastic stability of a new extended filtering for nonlinear systems subject to measurement packet losses. The measurements sensored are transmitted to remote estimator through a packet-dropping network. By introducing a time-stamped packet arrival indicator sequence, the measurement loss process is modeled as an independent, identically distributed (i.i.d.) and therefore a Bernoulli process. Boundedness of estimation error covariance matrices is studied by verifying existence of a critical threshold for measurement packet arrival probability. It is also shown that, under appropriate assumptions, the estimation error remains bounded as long as noise covariance matrices and initial estimation error can be ensured small enough.