Name
Abstract | ||
|---|---|---|
This paper studies fully distributed parameter estimation under measurement attacks. A connected network of agents makes measurements of a parameter while an adversary manipulates a subset of the measurements. The goal of the agents is to recover the parameter in the presence of measurement attacks. This paper presents an iterative consensus+innovations algorithm for resilient distributed estimation. The algorithm ensures that all agents correctly recover the parameter of interest, with exponentially fast rate of convergence, so long as less than 3/10 of the agents' measurements are under attack, regardless of the (connected) network topology. We demonstrate the performance of the algorithm through numerical examples. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CDC.2018.8619746 | 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC) |
Field | DocType | ISSN |
Mathematical optimization,Computer science,Algorithm,Network topology,Rate of convergence,Adversary,Estimation theory,Exponential convergence,Exponential growth | Conference | 0743-1546 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Chen | 1 | 69 | 6.73 |
| Soummya Kar | 2 | 1874 | 115.60 |
| José M. F. Moura | 3 | 5137 | 426.14 |