Characterized by a cross-disciplinary nature, the bearing-based target
localization task involves estimating the position of an entity of interest by
a group of agents capable of collecting noisy bearing measurements. In this
work, this problem is tackled by resting both on the weighted least square
estimation approach and on the active-sensing control paradigm. Indeed, we
propose an iterative algorithm that provides an estimate of the target position
under the assumption of Gaussian noise distribution, which can be considered
valid when more specific information is missing. Then, we present a seeker
agents control law that aims at minimizing the localization uncertainty by
optimizing the covariance matrix associated with the estimated target position.
The validity of the designed bearing-based target localization solution is
confirmed by the results of an extensive Monte Carlo simulation campaign