Particle filtering for nonlinear cyber–physical systems under Round-Robin protocol: Handling complex sensor issues and non-Gaussian noise

Abstract

This paper proposes particle filtering for state estimation considering Round-Robin protocol for discrete-time nonlinear cyber–physical systems with non-Gaussian noise affecting the channels and multiple complex sensor phenomena, including missing measurements (MMs) and randomly occurring sensor saturations (ROSSs). A novel energy harvesting sensor is applied to ensure uninterrupted measurement transmission, and a simplified energy-transfer recursive algorithm is proposed to further calculate the measurement transmission probability of energy harvesting sensors. In addition, considering actual engineering scenarios, two sequences of Bernoulli-distributed random variables with known probability distributions are employed to describe the characteristics of MMs and ROSSs. During the design process of the filtering scheme, we construct a modified likelihood function to compensate for the impact of MMs, ROSSs, and energy harvesting sensors in cyber–physical systems. Subsequently, based on the mathematical characterisation of the likelihood function, we propose a particle filtering algorithm that can address the difficulty in obtaining the likelihood function when MMs and ROSSs occur simultaneously. Finally, the usefulness of the proposed particle filtering method is validated using two tracking examples.