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• W2065935304 abstract "Sensor fusion has found a lot of applications in today's industrial and scientific world with Kalman filtering being one of the most practiced methods. Despite their simplicity and effectiveness, Kalman filters are usually prone to uncertainties in system parameters and particularly system noise covariance. This paper proposes a Kalman filtering framework for sensor fusion, which provides robustness to the uncertainties in the system parameters such as noise covariance and state initialization. Two methods are developed based on the proposed approach. The effectiveness of the proposed methods is verified through numerous simulations and experiments." @default.
• W2065935304 created "2016-06-24" @default.
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• W2065935304 date "2015-06-01" @default.
• W2065935304 modified "2023-10-16" @default.
• W2065935304 title "A Kalman Filter-Based Framework for Enhanced Sensor Fusion" @default.
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• W2065935304 doi "https://doi.org/10.1109/jsen.2014.2388153" @default.
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