A multi-temporal multi-sensor circular fusion filter

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 recursive circular filter that provides estimates of the parameters of a model state is proposed. The proposed methodology is assessed using both synthetic and real data. The real data are obtained from a magnetometer and a gyroscope.