| dc.contributor.advisor | Jamie Anderson. | en_US |
| dc.contributor.author | Korka, David A. (David Andrew), 1976- | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2005-08-22T20:44:24Z | |
| dc.date.available | 2005-08-22T20:44:24Z | |
| dc.date.copyright | 1999 | en_US |
| dc.date.issued | 1999 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/9380 | |
| dc.description | Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999. | en_US |
| dc.description | Includes bibliographical references (p. 65). | en_US |
| dc.description.abstract | This thesis develops a Kalman filter which integrates the inertial navigation system of the Vorticity Control Unmanned Undersea Vehicle (VCUUV) with redundant navigation sensor measurements. The model for the Kalman filter uses redundant measurements in a feedback loop to better estimate navigation variables. Using outputs from the Inertial Measurement Unit (IMU) and from a depth sensor, a velocity sensor and a magnetometer, a Kalman filter is developed. Actual test runs on the VCUUV prove the new system superior to the previously used open-loop navigation system. | en_US |
| dc.description.statementofresponsibility | by David A. Korka. | en_US |
| dc.format.extent | 69 p. | en_US |
| dc.format.extent | 3989324 bytes | |
| dc.format.extent | 3989084 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Kalman filtering for aided inertial navigation system | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | S.B.and M.Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 44878364 | en_US |
