Name
Abstract | ||
|---|---|---|
A generalized state space representation of a dynamical system with random modes is presented. The dynamics equation includes the effect of the state's linear minimum mean squared error (LMMSE) optimal estimate, representing the behavior of a closed loop control system featuring a state estimator. The measurement equation is allowed to depend on past LMMSE estimate of the state, which can be used to represent the fact that measurements are obtained from a validation window centered at the predicted measurement position and not from the entire surveillance region. The matrices comprising the system's mode constitute an independent stochastic process. It is shown that the proposed formulation generalizes several important problems considered in the past, and allows a unified modeling of new ones. The LMMSE optimal filter is derived for the considered general problem and is shown to reduce, in some special cases, to some well known classical algorithms. The new concept, as well as the derived algorithm, are demonstrated for the problem of target tracking in clutter, and are shown to attain performance that is competitive to that of several popular nonlinear methods. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CDC.2011.6161105 | Decision and Control and European Control Conference |
Keywords | Field | DocType |
closed loop systems,least mean squares methods,nonlinear dynamical systems,state estimation,stochastic processes,target tracking,LMMSE estimation,LMMSE optimal filter,closed loop control system,dynamical system,dynamics equation,generalized state space representation,independent mode transition,independent stochastic process,measurement equation,measurement position,nonlinear method,state estimator,state linear minimum mean squared error optimal estimation,surveillance region,target tracking | Mathematical optimization,Noise measurement,Clutter,Computer science,Control theory,Matrix (mathematics),Nonlinear methods,State-space representation,Minimum mean square error,Stochastic process,Dynamical system | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-61284-799-3 | 978-1-61284-799-3 | 2 |
PageRank | References | Authors |
0.48 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Sigalov | 1 | 2 | 0.82 |
| Tomer Michaeli | 2 | 255 | 20.61 |
| Y. Oshman | 3 | 15 | 2.75 |