This paper considers a human-autonomy collaborative sensor data 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.

翻译：本文提出了一种基于贝叶斯框架的人机协同传感器数据融合方法，用于动态目标定位。为弥补自主跟踪系统的不足，我们设计采集视觉监控目标的人类操作员提供的空间感知信息——这些信息以自由手绘草图形式标注目标所在区域。本文的核心贡献在于构建人类输入的自适应概率模型，其中自适应参数可捕捉人类输入的可靠性水平。进一步的创新是提出了一种新型联合贝叶斯学习方法，该方法在融合人类与自主传感器数据时，能够同时捕捉并考量人类检测可靠性的动态变化。该贝叶斯建模框架的独特之处在于其解析形式的闭环更新方程，显著提升了计算效率。仿真实验验证了方法有效性。