Multi-Sensor Data Fusion in Non-Gaussian Orbit Determination

Multi-sensor networks can alleviate the need for high-cost, high-accuracy, single-sensor tracking in favor of an abundance of lower-cost and lower-accuracy sensors to perform multi-sensor tracking. The use of a multi-sensor network gives rise to the need for a fusion step that combines the outputs of all sensor nodes into a single probabilistic state description. When considering Gaussian uncertainties, the well-known covariance intersection technique may be used. In the more general, non-Gaussian case, covariance intersection is not sucient. This paper examines a fusion method based on logarithmic opinion pools and develops algorithms for multi-sensor data fusion as well as investigates weight selection schemes for the opinion pool. The proposed fusion rules are applied to the tracking of a space object using multiple ground-based optical sensors. Non-Gaussian orbit determination methods are applied to each sensor individually, and the fusion rule is applied to the combined outputs of each sensor node. It is shown that the multi-sensor fusion rule leads to an increase of nearly two orders of magnitude in the position tracking accuracy as compared to the traditional single-sensor tracking method.