Nonlinear Filtering With Sample-Based Approxi-mation Under Constrained Communication:Progress,Insights and TrendsOACSTPCDEI

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements.In recent years,the advance of network communication technology has not only popularized the net-worked systems with apparent advantages in terms of installation,cost and maintenance,but also brought about a series of chal-lenges to the design of nonlinear filtering algorithms,among which the communication constraint has been recognized as a dominating concern.In this context,a great number of investiga-tions have been launched towards the networked nonlinear filter-ing problem with communication constraints,and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios.The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints.More specifically,we first review three important families of sample-based filtering methods known as the unscented Kalman filter,particle filter,and maximum correntropy filter.Then,the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information,limited resources and cyber security.Finally,several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.