UAPx is devoted to identifying and classifying the presently unidentified, unknown, and unclassified. While it is open to the possibility of techno-signatures in the atmosphere, it does not exclude other possibilities or prefer the most speculative ones, still respecting Sagan’s dictum regarding extraordinary claims and evidence. Its main goals are:
Take data that fills the gap in the scientific knowledge of this phenomenon due to a lack of high-quality, unclassified data caused by decades of taboo and over-classification.
Fully publicize said data, but only after analyses are complete, where completion is defined as the publication of peer-reviewed articles in (ideally existing, high-impact) journals, further reducing stigma by lending more credibility to the topic of UAP.
Combine existing hardware in new ways and develop new sensors to accomplish the first two goals.
To Accomplish This...
In service of all three of these objectives, UAPx has developed and continues to refine, in an iterative fashion, methods for the collection of quality data on UAP through the use of a small-scale (table-top) but diverse, advanced, and multi-spectral suite of sensors, covering not only the EM spectrum but also EM fields, ionizing radiation, and other types of measurements, buttressed by calibrations before, during, and after field deployments.
We take our cue from multi-messenger astronomy, pursuing a form of “multi-messenger UAP Studies,” through use of a diverse set of sensing devices. Such a strategy involves:
Commissioning multiple cameras and multiple copies of the sensors,
to capture the same phenomenon from different angles, at high resolution, for robust estimates of distance, size, speed, and acceleration via triangulation made possible by precise instrument locations.
Coincidence timing across all devices to help in faster data reduction down to lists of ambiguities and true “anomalies.”
The eventual construction of (semi-)permanent stations with automated, remote sensor suites, to serve as near-continuous data collection stations, in both control areas as well as alleged UAP hot spots.
Corroboration of alleged anomalies through use of public sources of data, including but not limited to Doppler weather radar data, unclassified satellite imagery, and global particle/radiation detection networks. This item ties into the coincidence timing, the second point above.
Monte Carlo computer simulations
Monte Carlo computer simulations, as well as both Bayesian and frequentist inference, are applied to determine the probability of two events overlapping in time due to accidental coincidence. AI/ML techniques (Artificial Intelligence and Machine Learning) also play an instrumental role in UAPx, for a non-binary, multi-stage classification, starting with motion detection in IR or visible light imagery.
Custom Target Analysis Protocol
UAPx Inc. developed a new software package for image processing, called C-TAP (Custom Target Analysis Protocol), capable of detecting discrete objects crossing a screen even in conditions of rolling clouds, through use of a naive Bayesian classifier applied primarily to minimum and maximum differences in pixels between frames and the standard deviation across the RGB pixel values, to help differentiate between camera noise and physical objects captured crossing the field of view.