A fast global node selection algorithm for bearings-only target localization

In the target tracking, the nodes aggregate their observations of the directions of arrival of the target. The network then uses an extended Kalman filter (EKF) to combine the measurements from multiple snapshots to track the target. In order to rapidly select the best subset of nodes to localize the target with the minimum mean square position error and low power consumption, this paper proposes a simple algorithm, which uses the location information of the target and the network. The lower bound of localization error is utilized according to the distances between the target and the selected active nodes. Furthermore, the direction likelihoods of the active nodes is predicted by way of the node/target bearing distributing relationships.