The effort to detect and understand Unidentified Aerial Phenomena (UAP) has gained momentum in recent years, driven by declassified government reports and increased scientific interest. The US Director of National Intelligence (DNI) has highlighted the need for better data collection, noting that many UAP sightings remain unexplained due to sensor limitations and inconsistent methodologies.
Image Credit: HelloRF Zcool/Shutterstock.com
This article examines whether modern sensor technologies—spanning radar, infrared imaging, and artificial intelligence—are sophisticated enough to bridge these gaps. Can emerging innovations in multi-domain detection and machine learning finally provide definitive answers, or are we still missing key technological breakthroughs?
Can Technology Finally Solve the UAP Mystery? Get the latest insights on AI, radar, and multi-sensor detection—download the full guide now!
Current Sensor Technologies in UAP Detection
One of the biggest challenges in understanding UAPs is that we’re still relying on detection methods designed for conventional aircraft—things like radar and optical tracking that weren’t built for fast, unpredictable movements. While technology has come a long way, with advances in radar, infrared imaging, and AI-driven analysis, there are still some major gaps in our ability to reliably track and analyze these unknown objects.
Radar Systems: Passive vs. Active Approaches
Radar has been a go-to tool for tracking things in the sky for decades, but when it comes to UAPs, it has some real limitations.
Active radar, which works by sending out electromagnetic pulses and analyzing the reflections, is great for detecting conventional aircraft. But it has a blind spot: fast-moving objects can easily slip between radar sweeps, making them hard to track in real-time. Plus, while meteorological radars are widely available, they update too slowly to be useful for spotting quick or unpredictable movement.
Passive radar, on the other hand, takes a different approach. Instead of broadcasting its own signal, it detects disruptions in existing signals, like radio or TV waves. This makes it not only cheaper but also stealthier—ideal for monitoring the sky without broadcasting its presence. The SkyWatch network is one effort aiming to use this tech to create a continuous, large-scale UAP detection system.
Some researchers are already experimenting with passive radar for UAP detection. UAP Tracker, for example, uses low-cost RTL2832U dongles to build a network of passive forward scatter radar sensors. This kind of setup could help create a scalable, crowdsourced tracking system, offering broader coverage than traditional radar alone.1
Infrared and Multi-Spectral Imaging
Infrared and multi-spectral imaging have become key tools for spotting UAPs, especially in conditions where regular cameras struggle—like at night or in bad weather. A great example is The Galileo Project, a Harvard-led effort that uses eight FLIR Boson 640 infrared cameras at an observatory in Massachusetts. These cameras work alongside real-time ADS-B flight tracking data, making it easier to separate normal aircraft from unidentified objects.
In just five months, this system recorded 500,000 aerial trajectories, flagging 16 % as potential anomalies. But even with this high-tech setup, it’s not perfect. Cloud cover and atmospheric interference can mess with the data, and initial tests showed a frame-by-frame detection success rate of only 36 %. To improve accuracy, researchers are refining their calibration techniques and testing multi-spectral imaging, which could help cut through environmental noise.
The long-term goal? Smarter machine learning algorithms that can filter out false positives and improve real-time anomaly detection. If they get it right, infrared-based tracking could become a much more reliable tool for spotting UAPs, even in less-than-ideal conditions.2
Dr. Avi Loeb, "New Results from the Galileo Project Observatory and the Pacific Expedition"
AI-Driven Analysis and Computer Vision
With so much data being collected, AI is becoming a crucial part of UAP research. The Galileo Project is using advanced computer vision and tracking algorithms like You Only Look Once (YOLO) and Simple Online and Realtime Tracking (SORT) to analyze footage, reconstruct flight paths, and categorize objects.
But AI still has a ways to go. One big challenge is incomplete trajectory reconstruction—basically, not having enough data to confidently determine what an object is. That’s where HyperNeuron, a machine learning model designed to detect signal anomalies, comes in. It’s helping researchers filter out sensor glitches and external interference, making it easier to focus on real UAPs instead of false alarms.
As AI models get better, they’ll be able to distinguish between genuine unknowns and everyday objects like birds, drones, or commercial aircraft. The hope is that, with improved training and data refinement, AI will become a reliable, large-scale tool for spotting and analyzing UAPs.3
Persistent Challenges in UAP Detection
Despite advancements in sensor technology, detecting and analyzing Unidentified Aerial Phenomena (UAPs) remains a difficult task. A variety of factors—environmental interference, sensor resolution limitations, and the lack of multi-domain tracking—continue to hinder accurate identification. Overcoming these obstacles requires more precise calibration, standardized methodologies, and integrated detection frameworks that bring together data from multiple sources.
Data Quality and Environmental Interference
One of the biggest hurdles in UAP research is filtering out environmental noise. A recent study in Scientific Reports analyzed 98,000 UAP reports and found strong correlations between sightings and factors like light pollution, cloud cover, and proximity to military installations. Urban areas, for instance, tend to have more reported sightings—not necessarily because there are more UAPs, but because the dense air traffic increases the chances of misidentifications.
To improve accuracy, sensors need to account for these confounding variables through better calibration and multi-modal validation. That means developing adaptive systems that can automatically filter out common aerial objects like drones, weather balloons, and birds—helping researchers focus on genuinely anomalous events.4
Sensor Resolution and Classification Ambiguity
Even cutting-edge detection systems struggle with accurate classification of UAPs. The Galileo Project, for example, flagged 144 ambiguous trajectories out of 80,000 recorded anomalies—primarily due to missing key kinematic data like exact distance and velocity. Without this information, it’s hard to tell whether an object is truly unexplained or just an aircraft viewed from an unusual angle.
Radar systems have their own classification issues. Passive radar setups, while useful for detecting anomalies, lack the time resolution needed to accurately track fast-moving or maneuvering objects. Active radar provides sharper data, but its findings are often restricted due to security and regulatory concerns. This lack of publicly available, high-resolution radar data makes it difficult to separate actual UAPs from everyday airborne objects, leaving many cases unresolved.2
Integration Across Domains
One of the biggest mysteries surrounding UAPs is their reported ability to transition between air, water, and even space. The problem? Most sensors are built to monitor just one environment—for example, air traffic radars aren’t designed to track objects moving underwater, and satellite imaging isn’t optimized for real-time atmospheric anomalies. This makes it incredibly difficult to follow objects that seamlessly shift between different domains.
To tackle this issue, the US All-Domain Anomaly Resolution Office (AARO) is pushing for the development of multi-domain detection frameworks. The idea is to integrate sonar, radar, and optical imaging into a unified system that can track objects across multiple environments. If successful, this approach could provide a much clearer picture of UAP behavior—offering new insights into their movement patterns and potential origins.4
So, where do we go from here?
Innovations Bridging the Gap
Advancements in detection technology and data analysis are gradually improving how we track and study unidentified aerial phenomena. But technology alone isn’t enough—progress depends on better data integration, broader participation, and stronger collaboration across scientific and governmental institutions. Researchers are now focusing on multi-sensor systems, public involvement, and interdisciplinary cooperation to refine detection accuracy and expand research beyond traditional frameworks.
One promising approach is multi-sensor observatories, like those being developed by the Galileo Project, which combine infrared, optical, and radio sensors to cross-validate detections. This reduces the limitations of any single method—infrared struggles with weather interference, for example, but corroborating that data with radio signals can provide a clearer picture. Expanding these observatories globally could standardize research methods and improve long-term statistical analysis of UAP activity.
At the same time, crowd-sourced networks and low-cost detection systems are making UAP research more accessible. Initiatives like SkyWatch use passive radar receivers to gather real-time data, allowing independent researchers to contribute to a growing body of evidence. Mobile apps connected to sensor arrays could take this a step further, alerting users to nearby anomalies and enabling real-time, multi-sensor documentation of UAPs.
Meanwhile, government and academic collaborations are pushing the boundaries of UAP research. NASA, the Department of Defense, and institutions like MIT’s Lincoln Lab are developing AI-driven tools to filter out radar clutter and identify potential anomalies with greater precision. These partnerships help bridge the gap between classified military data and open scientific inquiry, allowing for more rigorous and transparent analysis.
Moving forward, standardizing data collection and enhancing sensor fusion will be critical. Combining LiDAR, hyperspectral imaging, and quantum radar could address existing resolution gaps, while quantum sensors offer extreme precision even in challenging conditions. Expanding sensor networks in low-light pollution areas and fostering global cooperation could further enhance detection rates and lead to more consistent monitoring.
Ultimately, the challenge isn’t just detecting UAPs—it’s understanding them. By improving how we collect, share, and analyze data, we might finally start moving from speculation toward concrete explanations about what’s really in our skies.1,2,4
The Path Forward
For UAP research to progress, standardizing data collection and improving data-sharing frameworks is essential. Right now, different studies use different methodologies, making it difficult to compare findings or build a cohesive understanding. Unified reporting systems and publicly accessible databases would allow researchers to cross-reference data, minimize duplication, and collaborate more effectively. With a more structured approach, scientists can reduce inconsistencies, refine detection models, and develop a clearer picture of UAP activity.
Another key step is enhancing sensor fusion by integrating multiple detection technologies. Combining LiDAR, hyperspectral imaging, and quantum radar could help close the resolution gaps that make accurate tracking so difficult. Quantum sensors, in particular, offer extremely precise measurements, improving target detection even in environments where traditional sensors struggle. Expanding sensor networks—especially in low-light pollution areas—and fostering international collaboration would further strengthen detection capabilities, allowing for more comprehensive monitoring of UAPs.4
While technologies like AI, multi-spectral imaging, and passive radar have pushed UAP detection forward, significant challenges remain—from environmental interference to classification ambiguities and limited cross-domain tracking. Projects like Galileo and SkyWatch demonstrate how integrated, open-source approaches can improve detection, but global cooperation and continued technological innovation are needed to reach definitive answers.
As Dr. Travis Taylor puts it, curiosity and cutting-edge science go hand in hand—and with the right advancements, today’s anomalies could become tomorrow’s discoveries.
Want to Learn More?
Interested in the latest breakthroughs in UAP detection technology? Explore these resources:
References and Further Reading
- Watters, W. A. et al. (2023). The Scientific Investigation of Unidentified Aerial Phenomena (UAP) Using Multimodal Ground-based Observatories. Journal of Astronomical Instrumentation. DOI:10.1142/s2251171723400068. https://www.worldscientific.com/doi/full/10.1142/S2251171723400068
- Domine, L. et al. (2025). Commissioning an All-Sky Infrared Camera Array for Detection of Airborne Objects. Sensors, 25(3), 783. DOI:10.3390/s25030783. https://www.mdpi.com/1424-8220/25/3/783
- Abualgasim, S. D., & Ahmed, Z. E. (2025). Phishing Detection Methods. In Critical Phishing Defense Strategies and Digital Asset Protection (pp. 25–48). IGI Global. DOI:10.4018/979-8-3693-8784-9.ch002. https://www.igi-global.com/chapter/phishing-detection-methods/370359
- Medina, R.M. et al. (2023). An environmental analysis of public UAP sightings and sky view potential. Scientific Reports 13, 22213 (2023). DOI:10.1038/s41598-023-49527-x. https://www.nature.com/articles/s41598-023-49527-x
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.