Robust linear decentralized tracking of a time varying sparse parameter is studied in multiple-input multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, novel Bayesian learning-based Kalman filtering (SBL-KF) framework developed order to track the parameter. Subsequently, an optimization problem fo...

Estimating and detecting faults is crucial in ensuring safe efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation even more challenging. To address this challenge, article proposes a novel filter to estimate multiple fault signals for class discrete-time linear parameter-varying (LPV) The design formulated as an optimization problem sol...

In this paper, two new identification schemes based on VSS theory and sliding mode are developed to address the time-varying parameter identification problems. The proposed direct identification scheme can successfully estimate fast time-varying parameters when the process is linear in the parametric space. The proposed indirect identification scheme further exploits the possibility of estimati...