Full metadata
Title
Sensor management algorithms for measurement of diffusion processes
Description
Modern systems that measure dynamical phenomena often have limitations as to how many sensors can operate at any given time step. This thesis considers a sensor scheduling problem in which the source of a diffusive phenomenon is to be localized using single point measurements of its concentration. With a linear diffusion model, and in the absence of noise, classical observability theory describes whether or not the system's initial state can be deduced from a given set of linear measurements. However, it does not describe to what degree the system is observable. Different metrics of observability have been proposed in literature to address this issue. Many of these methods are based on choosing optimal or sub-optimal sensor schedules from a predetermined collection of possibilities. This thesis proposes two greedy algorithms for a one-dimensional and two-dimensional discrete diffusion processes. The first algorithm considers a deterministic linear dynamical system and deterministic linear measurements. The second algorithm considers noise on the measurements and is compared to a Kalman filter scheduling method described in published work.
Date Created
2016
Contributors
- Najam, Anbar (Author)
- Cochran, Douglas (Thesis advisor)
- Turaga, Pavan (Committee member)
- Wang, Chao (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vii, 46 pages : color illustrations
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.38594
Statement of Responsibility
by Anbar Najam
Description Source
Viewed on July 12, 2016
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2016
Note type
thesis
Includes bibliographical references (pages 44-46)
Note type
bibliography
Field of study: Electrical engineering
System Created
- 2016-06-01 08:43:47
System Modified
- 2021-08-30 01:23:42
- 2 years 8 months ago
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