Name
Title | ||
|---|---|---|
State estimators for systems with random parameter matrices, stochastic nonlinearities, fading measurements and correlated noises. |
Abstract | ||
|---|---|---|
Using the innovation analysis approach, the optimal linear state estimators, including the filter, predictor and smoother, in the linear minimum variance (LMV) sense are presented for a class of nonlinear discrete-time stochastic uncertain systems with fading measurements and correlated noises. Stochastic uncertainties of parameter matrices are depicted by correlated multiplicative noises. Stochastic nonlinearities are characterized by a known conditional mean and covariance. Different sensor channels have different fading measurement rates. The process and measurement noises are finite-step auto- and/or cross-correlated with each other. Two simulation examples verify the effectiveness of the proposed algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ins.2017.02.048 | Inf. Sci. |
Keywords | Field | DocType |
State estimator,Random parameter matrix,Stochastic nonlinearity,Fading measurement,Correlated noise,Innovation analysis approach | Minimum-variance unbiased estimator,Applied mathematics,Discrete mathematics,Nonlinear system,Multiplicative function,Matrix (mathematics),Fading,Control theory,Conditional expectation,Mathematics,Estimator,Covariance | Journal |
Volume | Issue | ISSN |
397 | C | 0020-0255 |
Citations | PageRank | References |
13 | 0.55 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuli Sun | 1 | 734 | 52.41 |
| Tian Tian | 2 | 86 | 18.09 |
| Honglei Lin | 3 | 67 | 9.27 |