  Bearing--only estimation is one of the fundamental and challenging problems
in target tracking. As in the case of radar tracking, the presence of offset or
position biases can exacerbate the challenges in bearing--only estimation.
Modeling various sensor biases is not a trivial task and not much has been done
in the literature specifically for bearing--only tracking. This paper addresses
the modeling of offset biases in bearing--only sensors and the ensuing
multitarget tracking with bias compensation. Bias estimation is handled at the
fusion node to which individual sensors report their local tracks in the form
of associated measurement reports (AMR) or angle-only tracks. The modeling is
based on a multisensor approach that can effectively handle a time--varying
number of targets in the surveillance region. The proposed algorithm leads to a
maximum likelihood bias estimator. The corresponding Cram\'er--Rao Lower Bound
to quantify the theoretical accuracy that can be achieved by the proposed
method or any other algorithm is also derived. Finally, simulation results on
different distributed tracking scenarios are presented to demonstrate the
capabilities of the proposed approach. In order to show that the proposed
method can work even with false alarms and missed detections, simulation
results on a centralized tracking scenario where the local sensors send all
their measurements (not AMRs or local tracks) are also presented.
