Fusion Methodologies for Orbit Determination with Distributed Sensor Networks
James S. McCabe, Kyle J. DeMars · 2018
Given that a single ground-based sensor, such as a radar or electro-optical telescope, is limited to observing only a small portion of an object's orbit, tracking accuracy can be greatly improved by collecting data with multiple geographically disparate sensors. Processing the data provided by such a distributed sensor network, however, poses complications in that full cooperation, i.e. direct sharing of raw measurement data, is usually implausible. Alternatively, cooperation within the network can be more feasibly established by instead sharing the posterior state densities produced by each sensor's tracking scheme and fusing these densities directly. This paper investigates the use of geometric averaging approaches to probability density fusion to exploit the diversity of a cooperative, distributed sensor network. These methods not only require approximate methods to perform sensor fusion, but they also require numerical procedures to determine an ideal weighting for each density. Computationally efficient approximations to these fusion techniques are formulated and compared to more expensive methods to determine the efficacy of the approximations. A numerical simulation considering the tracking of a space object in low Earth orbit with three cooperating ground-based radar stations is presented to produce conclusions on the discussed approaches.