Name
Abstract | ||
|---|---|---|
The problem of estimating the state of a discrete-time linear system can be addressed by minimizing an estimation cost function dependent on a batch of recent measure and input vectors. This problem has been solved by introducing a receding-horizon objective function that includes also a weighted penalty term related to the prediction of the state. For such an estimator, convergence results and unbiasedness properties have been proved. The issues concerning the design of this filter are discussed in terms of the choice of the free parameters in the cost function. The performance of the proposed receding-horizon filter is evaluated and compared with other techniques by means of a numerical example. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TAC.2003.809155 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
Linear systems,State estimation,Filters,Cost function,Estimation error,Equations,Nonlinear dynamical systems,Stochastic resonance,Noise measurement,Maximum likelihood estimation | Convergence (routing),Linear dynamical system,Mathematical optimization,Optimal control,Linear system,Control theory,Discrete time nonlinear systems,Horizon,Mathematics,Free parameter,Estimator | Journal |
Volume | Issue | ISSN |
48 | 3 | 0018-9286 |
Citations | PageRank | References |
56 | 2.94 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Alessandri | 1 | 312 | 27.63 |
| M. Baglietto | 2 | 288 | 23.19 |
| G. Battistelli | 3 | 171 | 11.88 |