Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Brief Constrained linear state estimation-a moving horizon approach
Automatica (Journal of IFAC)
Adaptive Sampling for Linear State Estimation
SIAM Journal on Control and Optimization
Nonlinear Programming: Theory and Algorithms
Nonlinear Programming: Theory and Algorithms
| Hi-index | 22.14 |
The event-triggered state estimation problem for linear time-invariant systems is considered in the framework of Maximum Likelihood (ML) estimation in this paper. We show that the optimal estimate is parameterized by a special time-varying Riccati equation, and the computational complexity increases exponentially with respect to the time horizon. For ease in implementation, a one-step event-based ML estimation problem is further formulated and solved, and the solution behaves like a Kalman filter with intermittent observations. For the one-step problem, the calculation of upper and lower bounds of the communication rates from the process side is also briefly analyzed. An application example to sensorless event-based estimation of a DC motor system is presented and the benefits of the obtained one-step event-based estimator are demonstrated by comparative simulations.