To date there is little publicly available scientific data on Unidentified Aerial Phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal ground-based observatory to continuously monitor the sky and conduct a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave infrared FLIR Boson 640 cameras. Their calibration includes a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance-Broadcast (ADS-B) data. We establish a first baseline for the system performance over five months of field operation, using a real-world dataset derived from ADS-B data, synthetic 3-D trajectories, and a hand-labelled real-world dataset. We report acceptance rates (e.g. viewable airplanes that are recorded) and detection efficiencies (e.g. recorded airplanes which are successfully detected) for a variety of weather conditions, range and aircraft size. We reconstruct $\sim$500,000 trajectories of aerial objects from this commissioning period. A toy outlier search focused on large sinuosity of the 2-D reconstructed trajectories flags about 16% of trajectories as outliers. After manual review, 144 trajectories remain ambiguous: they are likely mundane objects but cannot be elucidated at this stage of development without distance and kinematics estimation or other sensor modalities. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers count for the five-month interval at a 95% confidence level. This likelihood-based method to evaluate significance is applicable to all of our future outlier searches.

翻译：迄今为止，关于不明空中现象（UAP）的公开科学数据极少，据称其特性与运动学特征超出了已知现象的已知性能范围。为弥补这一不足，伽利略计划正在设计、建造并调试一套多模态地面观测站，以持续监测天空并对所有空中现象（包括自然和人为现象）进行严格的长期空中普查。关键仪器之一是一套使用八台非制冷长波红外FLIR Boson 640相机的全天域红外相机阵列。其校准包括一种新颖的外参校准方法，该方法利用来自自动相关监视广播（ADS-B）数据的飞机位置信息。基于一个源自ADS-B数据、合成三维轨迹以及人工标注的真实世界数据集，我们建立了该系统在五个月现场运行期间性能的首个基线。我们报告了在各种天气条件、距离和飞机尺寸下的接受率（例如，被记录的可视飞机）和检测效率（例如，被成功检测到的已记录飞机）。我们从该调试期间重建了约50万条空中物体的轨迹。一项专注于二维重建轨迹大弯曲度的简易异常值搜索将约16%的轨迹标记为异常值。经过人工审查，有144条轨迹仍不明确：它们很可能是普通物体，但在当前开发阶段，若没有距离与运动学估计或其他传感器模态，则无法阐明。我们观测到的模糊异常值数量结合系统不确定性，得出在95%置信水平下，这五个月期间异常值数量的上限为18,271个。这种基于似然的方法来评估显著性适用于我们未来所有的异常值搜索。