An Optimization Framework for Resilient Batch Estimation in Cyber-Physical Systems

This article proposes a class of resilient state estimators for linear time-varying discrete-time systems. The dynamic equation the system is assumed to be affected by bounded process noise. As available measurements, they are potentially corrupted noise both dense and impulsive natures. latter, in addition being arbitrary its form, need not strictly bounded. In this setting, we construct estimator as set-valued map, which associates with measurements minimizing set some appropriate performance functions. We consider family such functions, each yielding specific instance proposed general estimation framework. It then shown that enjoys property resilience, i.e., it induces an error, which, under certain conditions, independent extreme values (impulsive) measurement Hence, error may bounded, while virtually unbounded. Moreover, provide several bounds (in different configurations), whose expressions depend explicitly on degree observability observed considered function. Finally, few simulation results provided illustrate resilience property.