Name
Abstract | ||
|---|---|---|
In this paper we will provide methods to calculate different types of Maximum A Posteriori (MAP) estimators for jump Markov systems. The MAP estimators that will be provided are calculated on the basis of a running Particle Filter (PF). Furthermore, we will provide convergence results for these approximate or particle based estimators. We will show that the approximate estimators convergence in distribution to the true MAP values of the stochastic variables. Additionally, we will provide an example based on tracking closely spaced objects in a binary sensor network to illustrate some of the results and show their applicability. |
Year | Venue | Keywords |
|---|---|---|
2009 | FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4 | Particle filters,Maximum a Posteriori Estimators,Dynamical Systems,Target Tracking |
Field | DocType | Citations |
Convergence (routing),Convergence of random variables,Applied mathematics,Random variable,Markov process,Computer science,Particle filter,Artificial intelligence,Mathematical optimization,Markov chain,Maximum a posteriori estimation,Machine learning,Estimator | Conference | 2 |
PageRank | References | Authors |
0.54 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Boers | 1 | 135 | 18.13 |
| Hans Driessen | 2 | 59 | 7.31 |
| Arunabha Bagchi | 3 | 58 | 10.78 |