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A multi-temporal multi-sensor circular fusion filter
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DOI：10.1016/j.inffus.2013.05.012.png)
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In many multi-sensor applications, noisy angular data are modeled as random variables which are commonly defined on the linear domain while the classical fusion and filtering are used to process their realizations. Weighted sum and Kalman filter are indeed the common approaches used to fuse and filter the data in most cases. These approaches are limited by the periodic nature of the angular data. In this article, the error of angular measurements is assumed to follow a von Mises distribution. Under this assumption, a multi-sensor fusion operator is proposed. Under the same assumption, a a magnetometer and a gyroscope. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Circular statistic
Bayesian estimation
Fusion
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