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Addendum: Pinto, M.; Gámez, N.; Fuentes, L.; Amor, M.; Horcas, J.M.; Ayala, I. Dynamic Reconfiguration of Security Policies in Wireless Sensor Networks. Sensors 2015, 15, 5251–5280
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Sensors 2015, 15(7), 16833-16847; doi:10.3390/s150716833

Performance Evaluation and Requirements Assessment for Gravity Gradient Referenced Navigation

1
Department of Geoinformatics, University of Seoul, Seoul 130-743, Korea
2
Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA
3
Agency for Defense Development, Daejeon 151-742, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 5 June 2015 / Revised: 1 July 2015 / Accepted: 8 July 2015 / Published: 13 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1308 KB, uploaded 13 July 2015]   |  

Abstract

In this study, simulation tests for gravity gradient referenced navigation (GGRN) are conducted to verify the effects of various factors such as database (DB) and sensor errors, flight altitude, DB resolution, initial errors, and measurement update rates on the navigation performance. Based on the simulation results, requirements for GGRN are established for position determination with certain target accuracies. It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance. In particular, a DB and sensor with accuracies of 0.1 E and 0.01 E, respectively, are required to determine the position more accurately than or at a level similar to the navigation performance of terrain referenced navigation (TRN). In most cases, the horizontal position error of GGRN is less than 100 m. However, the navigation performance of GGRN is similar to or worse than that of a pure inertial navigation system when the DB and sensor errors are 3 E or 5 E each and the flight altitude is 3000 m. Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present. However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available. View Full-Text
Keywords: gravity gradient; GGRN; EKF; requirements analysis gravity gradient; GGRN; EKF; requirements analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, J.; Kwon, J.H.; Yu, M. Performance Evaluation and Requirements Assessment for Gravity Gradient Referenced Navigation. Sensors 2015, 15, 16833-16847.

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