  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 multi-source, mathematically
heterogenous, affecting the system dynamics via isomeric (additive,
multiplicative and recessive) channels, and deeply coupled with each other. In
traditional filtering schemes, the multi-source 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, a novel state
estimation methodology called {\it composite disturbance filtering} (CDF) has
been proposed, which deals with the multi-source, heterogenous, and isomeric
disturbances based on their specific characteristics. With the CDF, enhanced
anti-disturbance capability can be achieved via refined quantification,
effective separation, and simultaneous rejection and attenuation of the
disturbances. In this paper, 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 the CDF offers an effective tool for state
estimation, especially in the presence of multi-source heterogeneous
disturbances.
