This work examines the problem of fusing human operator observations with probabilistic information extracted by an automated data fusion system, in the context of dynamic multitarget track characterization for large-scale surveillance. This soft data fusion problem is challenging because human operator observation errors are difficult to calibrate a priori and in general exhibit subtle conditional dependencies. Furthermore, the efficacy of combining inputs from operators depends heavily on operator error characteristics as well as track characterization uncertainty. A hierarchical fully Bayesian probabilistic model is developed to explicitly account for human sensor quality and Markovian dependencies in human reports. This model is used to perform online Bayesian inference via Gibbs sampling to simultaneously update the data fusion system’s knowledge of human sensor characteristics and target type probabilities. A strategy is also developed for value of information assessments to automatically query human operators for inputs as needed. Efficient approximations of high-dimensional human sensor parameter posterior distributions via Dirichlet probability density function (pdf) moment matching and parameter tying are also developed to provide significant computational speedups with little information loss. Results for different simulated operator profiles and automated target characterization baselines show that fully Bayesian fusion improves track characterization performance, while intelligently accounting for uncertain operator characteristics.

这项工作研究了在大规模监视的动态多目标轨道表征的背景下，将操作员的观察结果与由自动数据融合系统提取的概率信息相融合的问题。该软数据融合问题具有挑战性，因为操作员的观察误差很难先验校准，并且通常表现出细微的条件依赖性。此外，合并来自操作员的输入的效率在很大程度上取决于操作员的错误特征以及轨迹特征的不确定性。开发了分层的完全贝叶斯概率模型，以明确说明人类报告中的人类传感器质量和马尔可夫依赖关系。该模型用于通过Gibbs采样执行在线贝叶斯推理，以同时更新数据融合系统对人体传感器特征和目标类型概率的了解。还开发了一种信息评估价值的策略，可以根据需要自动查询操作员的输入信息。还开发了通过Dirichlet概率密度函数（pdf）矩匹配和参数绑定的高维人体传感器参数后验分布的有效近似值，以提供显着的计算加速，而几乎没有信息丢失。不同模拟操作员资料和自动目标特征基线的结果表明，完全的贝叶斯融合改善了轨道特征性能，同时智能地解决了不确定的操作员特征问题。