Scalable Distributed State Estimation over Binary Sensor Networks with Energy Harvester

Abstract

This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks, where every binary sensor is equipped with an energy harvester. The input of every binary sensor considers the randomly occurring missing measurements. The differences between the real and estimated inputs of binary sensor are employed to derive useful information in order to address the insufficient information for estimation purpose. The information from neighboring nodes is transmitted only if its energy level is positive, where a random variable is introduced to formulate the energy level. By means of the available information, distributed estimator is constructed for each binary sensor and the desirable performance constraints is given for the dynamic characteristics of estimation errors within a finite time horizon. Sufficient conditions are established for the existence of desired distribution estimation quantities through local performance analysis methods. Also, the desired distributed estimator gains are calculated recursively, which means the desirable scalability. Ultimately, the viability and efficiency of the distributed scheme are exhibited through a practical illustration.