Distributed bearings-only tracking using the federated Kalman filter

In this paper, a novel approach for distributed bearings-only tracking is presented. In the past years, the literature on tracking has focused more and more on the distributed Kalman filter, which yields the optimal state estimate, given that the sensor model of all sensors in the system is known to each local processor. Since this condition is hardly feasible in practical applications, various approximations exist. A particular well performing and easy to implement approximation is the Federated Kalman filter, which was published 1990. The following paper extends this methodology to Gaussian mixtures. As a result, it can be applied to bearing measurements with a decomposed likelihood function, which transforms the measurement into a Gaussian mixture of position hypotheses.