Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

Abstract

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of Big Data era, the disturbances of complicated systems are physically multisource, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multisource heterogenous disturbances are usually simplified as a lumped one so that the “single” disturbance can be either rejected or attenuated. Since the pioneering work in 2012 (Guo and Cao, 2012), a novel state estimation methodology called composite disturbance filtering (CDF) has been proposed, which deals with the multisource, heterogenous, and isomeric disturbances based on their specific characteristics. With CDF, enhanced antidisturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this article, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g., alignment, localization, and navigation), and future research directions. In summary, it is expected that CDF offers an effective tool for state estimation, especially in the presence of multisource heterogeneous disturbances.