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：本文在贝叶斯框架下研究人机协同传感器数据融合方法，以实现动态目标定位。为弥补自主跟踪系统的不足，我们提出收集视觉监测目标的人工操作员提供的空间感知信息，其形式为圈定目标所在区域的自由草图。本文重点在于构建一种自适应概率模型来处理人工输入，其中自适应项可捕捉人工输入的可靠性水平。本文的第二个贡献是提出一种新颖的联合贝叶斯学习方法，以融合人工与自主传感器输入，同时捕捉并考虑人工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分析形式的闭合更新方程，赋予方法显著的计算效率。仿真结果验证了方法的有效性。