Event-Triggered State Estimation Using Particle Filtering Approach

In this paper, we design an event-trigger particle filter (ETPF) for a highly nonlinear process with noisy measurements. The latter are transferred over a wireless network. However, the classical send-on-delta is proposed as a mechanism for reducing the data transmitted from sensors to the remote state estimator. This mechanism helps save bandwidth and energy. It will be shown that by using ETPF the estimation quality can be improved, the effectiveness of the particle filtering approach is demonstrated and compared with Cubature Kalman Filter. Simulation on the two-link robot arm system verify the performance of the proposed approach.