  This paper presents a dynamic state observer design for discrete-time linear
time-varying systems that robustly achieves equalized recovery despite delayed
or missing observations, where the set of all temporal patterns for the missing
or delayed data is modeled by a finite-length language. By introducing a
mapping of the language onto a reduced event-based language, we design a state
estimator that adapts based on the history of available data at each step, and
satisfies equalized recovery for all patterns in the reduced language. In
contrast to existing equalized recovery estimators, the proposed design
considers the equalized recovery level as a decision variable, which enables us
to directly obtain the global minimum for the intermediate recovery level,
resulting in improved estimation performance. Finally, we demonstrate the
effectiveness of the proposed observer when compared to existing approaches
using several illustrative examples.
