  We consider a human-assisted autonomy sensor fusion for dynamic target
localization in a Bayesian framework. To compensate for the shortcomings of an
autonomous tracking system, we propose to collect spatial sensing information
from human operators who visually monitor the target and can provide target
localization information in the form of free sketches encircling the area where
the target is located. Our focus in this paper is to construct an adaptive
probabilistic model for human-provided inputs where the adaption terms capture
the level of reliability of the human inputs. The next contribution of this
paper is a novel joint Bayesian learning method to fuse human and autonomous
sensor inputs in a manner that the dynamic changes in human detection
reliability are also captured and accounted for. A unique aspect of this
Bayesian modeling framework is its analytical closed-form update equations,
endowing our method with significant computational efficiency. Simulations
demonstrate our results.
