Probability-Constrained Distributed Non-Fragile Estimation Over Sensor Networks Subject to Stochastic Communication Protocol

This is the authors' abstract. We don't add key points for this paper.

This article focuses on the probability-constrained distributed non-fragile (PDNF) estimation problem for nonlinear time-varying systems with unknown but bounded noises, sensor saturation and uniform quantization over sensor networks (SNs). Owing to the limited bandwidth resources, stochastic communication protocol (SCP) is employed to manage network transmission and prevent data collision. At each transmission instant, the sensor node is allowed to communicate with only one randomly selected neighboring sensor. Meanwhile, the non-fragility of the estimator is taken into account to handle potential parameter variations. The goal of this article is to develop a PDNF estimation algorithm such that 1) the estimation error is confined within a certain ellipsoidal region with a predefined probability; and 2) the resulting error ellipsoid is minimized in the sense of matrix trace to achieve optimal estimation performance. In light of this, the sufficient criteria for the availability of the estimator are derived through recursive linear matrix inequality (RLMI) technique. Furthermore, the optimal estimator parameters are attained by solving a convex optimization problem. Ultimately, two simulation experiments are presented to validate the feasibility and practicality of the designed estimation algorithm.