Remote State Estimation with Stochastic Event-triggered Sensor Schedule in the Presence of Packet Drops

This paper studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. It is shown that the system state conditioned on the available information at the estimator side is Gaussian mixture distributed. Minimum mean square error (MMSE) estimators are subsequently derived for both open-loop and closed-loop schedules. Since the optimal estimators require exponentially increasing computation and memory, sub-optimal estimators to reduce the computational complexities are further provided. In the end, simulations are conducted to illustrate the performance of the optimal and sub-optimal estimators.Comment: accepted by the 2019 American Control Conferenc

Stochastic Event-based Sensor Schedules for Remote State Estimation in Cognitive Radio Sensor Networks

We consider the problem of communication allocation for remote state estimation in a cognitive radio sensor network~(CRSN). A sensor collects measurements of a physical plant, and transmits the data to a remote estimator as a secondary user (SU) in the shared network. The existence of the primal users (PUs) brings exogenous uncertainties into the transmission scheduling process, and how to design an event-based scheduling scheme considering these uncertainties has not been addressed in the literature. In this work, we start from the formulation of a discrete-time remote estimation process in the CRSN, and then analyze the hidden information contained in the absence of data transmission. In order to achieve a better tradeoff between estimation performance and communication consumption, we propose both open-loop and closed-loop schedules using the hidden information under a Bayesian setting. The open-loop schedule does not rely on any feedback signal but only works for stable plants. For unstable plants, a closed-loop schedule is designed based on feedback signals. The parameter design problems in both schedules are efficiently solved by convex programming. Numerical simulations are included to illustrate the theoretical results.Comment: This paper was accepted by IEEE Transaction on Automatic Control and was published as an early access on July 7 202