Abstract
This paper addresses the multisensor estimation problem for both linear and nonlinear systems in a fully connected decentralized sensing architecture. The sensor data fusion problem is identified and the case for decentralized arhi-tectures, rather than hierarchical or centralized ones, is made. Fully connected decentralized estimation algorithms in both state and information spaces are then developed. The intent is to show that decentralized estimation is feasible and to demonstrate the advantages of information space over state space. The decentralization procedure is then repeated for the extended Kalman filter and extended information filter to produce decentralized filters for nonlinear systems. The four filters are compared and contrasted. In appraising the algorithms the problems associated with the requirement for a fully connected topology are identified.
| Original language | English |
|---|---|
| Pages (from-to) | 23-34 |
| Number of pages | 12 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 3209 |
| DOIs | |
| Publication status | Published - 1997 |
| Externally published | Yes |
| Event | Sensor Fusion and Decentralized Control in Autonomous Robotic Systems - Pittsburgh, PA, United States Duration: 14 Oct 1997 → 14 Oct 1997 |
Keywords
- Data fusion
- Decentralized
- Estimation
- Information filter
- Multisensor
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering