Stochastic stability of extended filtering for non-linear systems with measurement packet losses

This study is concerned with stochastic stability of a new extended filtering for non-linear systems subject to measurement packet losses. The measurements sensored are transmitted to the estimator through a packet-dropping network. By introducing a time-stamped packet arrival indicator sequence, the measurement loss process is modelled as an independent, identically distributed (i.i.d.) and therefore a Bernoulli process. The boundedness of estimation error covariance matrices is proved by showing the 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 the noise covariance matrices and the initial estimation error can be ensured small enough. Finally, simulation results validating the effectiveness of this proposed filtering framework are also presented.