Technology Category
Intelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Calibration & Uncertainty QuantificationData Mining & Knowledge Management: Anomaly/Outlier Detection
Commissioning An All-Sky Infrared Camera Array for Detection Of Airborne Objects
🤖 AI Summary
To address the scarcity of scientific observational data on Unidentified Aerial Phenomena (UAP), this work introduces the first multimodal ground-based infrared sky survey system: an all-sky array comprising eight FLIR Boson 640 long-wave infrared cameras, coupled with a novel real-time extrinsic calibration method leveraging ADS-B aircraft position data, integrating thermal-geometric joint calibration and 3D trajectory synthesis. We propose a lightweight anomaly detection framework based on trajectory sinuosity, augmented by statistical significance testing. Over a five-month systematic observational campaign, we establish the first multi-source, real-world infrared UAP dataset; achieve 41% field-of-view coverage for ADS-B aircraft capture and 36% frame-level detection efficiency; reconstruct ~500,000 trajectories, identify 80,000 anomalous trajectories—including 144 currently unattributable cases—and derive a 95% confidence upper bound of 18,271 statistically significant anomalous events.
Technology Category
Application Category
📝 Abstract
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, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via 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. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches.
Problem
Research questions and friction points this paper is trying to address.
Detects airborne objects using infrared cameras
Analyzes ambiguous aerial phenomena trajectories
Estimates upper limits for outlier counts
Innovation
Methods, ideas, or system contributions that make the work stand out.
All-sky infrared camera array
Extrinsic calibration using ADS-B
Trajectory outlier search method
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