Abstract
Information bounds and asymptotically optimal procedures for decentralized quickest change under different scenarios were discussed. The decentralized system with limited local memory, where the sensors do not have access to their past observations was considered. It was shown that in the decentralized decision system with Gaussian sensor observations, the detection delay of the monotone likelihood ratio quantizer (MLRQ) is at most π/2-1 ≈ 57% larger than that of the optimal centralized procedure. It was found that the method can be easily extended to non-Gaussian distributions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 249 |
| Number of pages | 1 |
| Journal | IEEE International Symposium on Information Theory - Proceedings |
| State | Published - 2004 |
| Event | Proceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States Duration: Jun 27 2004 → Jul 2 2004 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics