Modified three stage Kalman filtering for stochastic non-linearsystems with randomly occurring faults and intermittentmeasurements

Abstract

A robust three stage Kalman filtering problem is investigated in this article for non-linearsystems with stochastic non-linearities, random faults and intermittent missing measure-ments. Therefore, a more general system model is governed in which unknown stochas-tic non-linearities are considered in both the system state and measurement equations.The fault terms are included by stochastic coefficients. Also, the sensor measurementsoccur in a random behaviour that encounters stochastic missing of data for each sen-sor. Both faults and unknown inputs are covered in the proposed filter. Moreover, therobust features of the developed estimator resolve the problems arising from the need foraccurate models of faults and unknown inputs. Furthermore, any predetermined knowl-edge about the statistical characteristics of the above factors is not required. The aug-mented filters are mostly used to provide the fault diagnosis features, however, in thecurrent work, this filter is decoupled to reduce the computational volume attributed tothis approach. It is proved that the estimation error is ultimately bounded despite differentstochastic terms and inaccurate information about the fault and unknown inputs. Finally,an illustrative example is proposed to demonstrate the effectiveness of the developedmethod (7) (PDF) Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements. Available from: https://www.researchgate.net/publication/359710155_Modified_three_stage_Kalman_filtering_for_stochastic_non-linear_systems_with_randomly_occurring_faults_and_intermittent_measurements [accessed Aug 06 2022].