Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
osu1023465547.pdf (608.44 KB)
ETD Abstract Container
Abstract Header
Self-Calibration of Sensor Networks
Author Info
Patterson, Robert Matthew
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1023465547
Abstract Details
Year and Degree
2002, Master of Science, Ohio State University, Electrical Engineering.
Abstract
Unattended sensor networks are becoming increasingly valuable for many military and commercial applications. A number of sensors are distributed in a region of interest. These sensors have the ability to sense and record energy, process data, and communicate with a central information processor. The information gained from signal processing is often used for detecting, tracking, and identifying objects of interest. In certain circumstances, location and orientation information regarding these sensors is unknown after being placed in the scene. The problem considered in this thesis is how to locate and orient these sensors. We present methods for solving this problem using calibration source signals. Sources are distributed in the same region of interest, with their locations and signal emission times being unknown. Each sensor has the ability to generate direction-of-arrival (DOA) and time-of-arrival (TOA) estimates from the source signals. The goal is to estimate the locations and orientations of all sensors using these TOA and DOA measurements. We develop necessary conditions for solving the self-calibration problem and provide a maximum likelihood solution and corresponding location error estimate. A lower bound on calibration accuracy via the Cramer-Rao Bound is found. We also consider the problem of locating and orienting a network of unattended sensors using nominal location information in the form of a prior probability distribution function. We develop a Bayes approach to the calibration problem and compute accuracy bounds on the calibration procedure. A maximum a posteriori estimation algorithm is shown to achieve the accuracy bound. Results using both synthetic data and field measurements are presented.
Committee
Randolph Moses (Advisor)
Pages
74 p.
Keywords
SENSOR
;
Sensor and Source
;
SELF-CALIBRATION
;
DOA
;
SENSOR NETWORKS
;
TOA
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Patterson, R. M. (2002).
Self-Calibration of Sensor Networks
[Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1023465547
APA Style (7th edition)
Patterson, Robert.
Self-Calibration of Sensor Networks.
2002. Ohio State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1023465547.
MLA Style (8th edition)
Patterson, Robert. "Self-Calibration of Sensor Networks." Master's thesis, Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1023465547
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
osu1023465547
Download Count:
2,722
Copyright Info
© 2002, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.