Finite-Horizon State Estimation for Multiplex Networks with Random Delays and Sensor Saturations Under Partial Measurements

This paper addresses the finite-horizon state estimation problem for multiplex networks (MNs) subject to random delays and sensor saturations under the constraint of only partial node measurements. The random time-varying delays are modeled via Bernoulli-distributed variables, while a Markovian random access protocol dynamically governs the data transmission at each time step. To tackle this problem, we design a set of robust state estimators based on partial measurements, ensuring the prescribed finite-horizon $H_{\infty }$ performance. Sufficient conditions for the existence of these estimators are established. Subsequently, the estimator gains are derived by solving the matrix inequalities inherent in these conditions. Finally, convincing numerical simulations demonstrate the effectiveness and practical applicability of the proposed algorithm.