Event-Triggered Probability-Guaranteed State Estimation for Uncertain Complex Networks: A Compression-Decompression Framework.

The probability-guaranteed event-triggered state estimation problem is studied for a class of complex networks (CNs) with uncertain inner coupling strengths under compression-decompression mechanisms (CDMs). An event-triggered transmission scheme (ETTS) is introduced to regulate data exchange, while compression-induced distortions are explicitly incorporated into the estimation model. The theoretical objective is to establish probability-guaranteed boundedness of the estimation error over a finite horizon in the presence of stochastic coupling uncertainties. To this end, a truncated hyper-rectangle is constructed to characterize the uncertain parameters with a prescribed confidence level, thereby enabling a probabilistic reformulation of the performance requirement. By employing matrix inequality techniques, sufficient conditions are derived to ensure the boundedness of the estimation error in a probabilistic sense. The resulting conditions lead to a recursive estimator synthesis expressed in terms of linear matrix inequalities (LMIs). A numerical example is finally presented to verify the theoretical results.