Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing MeasurementsOACSTPCDEI

This paper focuses on the quadratic nonfragile fil-tering problem for linear non-Gaussian systems under multi-plicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple miss-ing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the origi-nal system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented sys-tem.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parame-ters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effec-tiveness of this newly established quadratic filtering algorithm.