A Fault-Tolerant Distributed Sensor Reconciliation Scheme based on Decomposed Steady-State Kalman Filter

This paper presents a Distributed Sensor Reconciliation (DSR) methodology based on the decomposition of a centralized steady-state Kalman Filter (KF), which is used to distributively estimate the state of a LTI plant in the presence of unpredictable sensor faults. A scenario where individual local measurements may not ensure system observability, but their collective combination does, is considered. To tackle this challenge, the proposed DSR scheme aims at hiding the faulty sensor measurements in achieving the locally state estimates and in making all of them converge to the true system's state. This is accomplished by fusing, at each agent site, both local measurements and communicated estimates from a subset of neighboring agents. The estimation scheme leverages sensor redundancy to enhance robustness against sensor faults, thereby mitigating both multiplicative and additive faults. Finally, theoretical guarantees regarding the stability of the scheme are provided and a simulation example is presented.