Chandan Mazumdar

Work place: Jadavpur University, Kokata, PIN-700032

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Research Interests: Information-Theoretic Security, Network Security, Information Security, Solid Modeling, Real-Time Computing

Biography

Chandan Mazumdar is a senior professor in the Department of Computer Science and Engineering of Jadavpur University, India. He is also the Coordinator of the Centre for Distributed Computing. His research interests include enterprise information security modeling, fault tolerance, risk management, and real-time systems

Author Articles
Stochastic Characterization of a MEMs based Inertial Navigation Sensor using Interval Methods

By Subhra Kanti Das Dibyendu Pal Virendra Kumar S. Nandy Kumardeb Banerjee Chandan Mazumdar

DOI: https://doi.org/10.5815/ijigsp.2015.07.04, Pub. Date: 8 Jun. 2015

The aim here remains to introduce effectiveness of interval methods in analyzing dynamic uncertainties for marine navigational sensors. The present work has been carried out with an integrated sensor suite consisting of a low cost MEMs inertial sensor, GPS receiver of moderate accuracy, Doppler velocity profiler and a magnetic fluxgate compass. Error bounds for all the sensors have been translated into guaranteed intervals. GPS based position intervals are fed into a forward-backward propagation method in order to estimate interval valued inertial data. Dynamic noise margins are finally computed from comparisons between the estimated and measured inertial quantities It has been found that the intervals as estimated by proposed approach are supersets of 95% confidence levels of dynamic errors of accelerations. This indicates a significant drift of dynamic error in accelerations which may not be clearly defined using stationary error bounds. On the other side bounds of non-stationary error for rate gyroscope are found to be in consistence with the intervals as predicted using stationary noise coefficients. The guaranteed intervals estimated by the proposed forward backward contractor, are close to 95% confidence levels of stationary errors computed over the sampling period.

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