Event-triggered Adaptive Distributed State Estimation by Using Active-Passive Sensor Networks

This paper proposes a novel event-triggered adaptive observer for each node in the heterogeneous sensor networks (HSNs) in order to estimate state vector of an unknown target or process by using the sensed output when the input to the target/ process is unknown. A subset of nodes in the HSN referred to as active nodes, can sense the target periodically, estimate the target state vector by using their adaptive observer and can communicate the estimated state vector of the target with the neighboring nodes including passive nodes only at event triggered instants. The adaptive observer parameters of active nodes are updated in a periodic fashion. A connected graph defines the local information exchange within the HSN. By using the criterion of collective observability, a novel distributed event-triggered adaptive estimation scheme is introduced where the nodes are allowed to have different sensor modalities. Using the Lyapunov analysis, uniform ultimate boundedness of the state estimation and the parameter estimation errors are demonstrated. Simulation results are included to validate the theoretical claims.