An Efficient Event-Based State Estimator for Linear Discrete-Time System With Multiplicative Measurement Noise

This letter addresses an event-based state estimation for a linear discrete-time system with a remote estimator, when measurements are affected by multiplicative noise. An innovation-based scheduling scheme is used to communicate measurements from the sensor to the remote estimator. A maximum a posteriori estimator is employed to deal with multiplicative noise, enabling the recursive equations for the updated state and its covariance. The sensor to estimator communication rate is derived in terms of a predefined threshold that is used to trigger the sensor measurement. The results proposed are validated using a numerical example and Monte Carlo simulations.