51. Nonlinear distributed estimation in correlated heavy-tailed noise
Invited abstract in session TB-4: Modeling, Simulation and Optimization, stream Large scale optimization and applications.
Thursday, 10:00 - 11:30Room: C105
Authors (first author is the speaker)
| 1. | Manojlo Vukovic
|
| University of Novi Sad, Faculty of Technical Sciences, Faculty of Sciences | |
| 2. | Dusan Jakovetic
|
| University of Novi Sad Faculty of Sciences | |
| 3. | Dragana Bajovic
|
| Faculty of Technical Sciences, Univ. of Novi Sad | |
| 4. | Soummya Kar
|
| Carnegie Mellon University |
Abstract
We consider distributed inference problems where N agents interconnected in a generic network collaborate to estimate an unknown constant vector parameter while continuously assimilating linear low-dimensional noisy observations of the parameter. Unlike most of existing studies, we focus on the scenario wherein both inter-agent communications and agent sensing are subject to mutually correlated, infinite-variance noises. We present nonlinear distributed estimators that provably work under this challenging setting. Analytical and numerical examples illustrate the findings.
Keywords
- Artificial intelligence based optimization methods and appl
- Large- and Huge-scale optimization
- Data driven optimization
Status: accepted
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