Abstract
In this study, an event-driven state estimator is designed for stochastic systems that contain unknown inputs and processes as well as correlated measurement noise. First, the event-triggered state estimator's gain is deduced by using the random stability theory and Lyapunov's function. Then, based on the results, the corresponding state estimation errors are calculated in mean square convergence. Second, the corresponding unknown inputs are inhibited by using output errors of the estimator. In addition, the corresponding event-driven transmission strategy is designed by using a quadratic performance index, which guarantees a good balance between the estimation error and the data transmission rate as well as prolonged service life of the sensor battery. Finally, numerical simulation tests verify that the designed event-driven state estimator can estimate the system's state effectively and extend the sensor's battery life by approximately 48%. The proposed algorithm also leads to reduced utilization of network resources to some degree.
Highlights
Wireless sensor networks have been widely used in areas such as intelligent transportation systems, environmental monitoring, and industrial automation [1]
We proposed an event-driven state estimator for stochastic wireless sensor systems under unknown inputs and correlated noise
Unknown inputs and correlated noise affect the state at time k þ 1 in the state model; correlated noise is defined as the process correlated noise at time k and the measurement correlated noise at time k þ 1
Summary
Wireless sensor networks have been widely used in areas such as intelligent transportation systems, environmental monitoring, and industrial automation [1]. Replacement of old batteries incurs high cost and new batteries are difficult to procure due to limited availability To solve these problems, an approach of reducing the rate of communication between sensors and estimators, and the battery energy consumption, has been proposed [2]. This paper presents an event-driven control strategy that considers unknown inputs and correlated measurement noise for detecting the anomalous events caused by them. For achieving this objective, the state estimator gain triggered by events is derived by a stochastic Lyapunov function using stochastic stability theory. The event-driven transmission strategy is designed on the basis of approximate secondary performance indicators
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