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Passive System for Detection and Identification of UAVs Using Multispectral/Hyperspectral Imaging Technologies



OBJECTIVE: Develop and demonstrate a passive multispectral/hyperspectral imaging system that can identify unmanned aerial vehicles (UAVs) with high probability of detection and low probability of false alarms by exploiting the unique combined signatures in both spatial and spectral domains.


The proliferation of the use of unmanned aerial vehicles (UAVs) of various sizes and shapes for defense, commerce, monitoring, and other applications has increased at a very expeditious pace. Along with their advantages in ease of operation and low cost, the widespread availability of UAVs has posed significant security threats in both defense and civilian arenas.

Various approaches have been explored to interdict UAVs [Refs 1-3]. However, these interdiction strategies typically presume that the drone has already been detected. As part of the effective UAV threat mitigation, it is first necessary to have the ability to detect and track UAVs in the airspace. Straightforward adoption of currently fielded airspace surveillance technologies will not suffice as UAVs are much smaller in physical size and fly slowly at lower altitudes. For instance, a conventional air surveillance radar system (operating at L-band or S-band) rely on the radar cross section (RCS) of an aircraft for detection, but this may not always provide reliable detection in case of UAVs. Even if a dedicated system is sensitive enough to detect an object like a small UAV, just RCS information alone is not adequate. Some birds are similar in physical size to small UAVs and fly at similar altitudes and speeds. Visual detection of UAVs does not effectively discriminate between a small UAV, a bird or a plastic bag caught in the wind.

Recent technological advances have made long-wave infrared (LWIR) and mid-wave infrared (MWIR) hyperspectral imaging (HSI) in the 3-5 and 8-12 micrometer wavelength ranges a viable technology in many demanding military application areas where materials can be identified by their spectral signatures. Further, LWIR spectral range offers advantages that are unmatched by the visible and short-wave infrared range as LWIR is not susceptible to performance degradation from scattering by water-based aerosols, dense fog and clouds in the atmosphere. Hence, the operational utility of LWIR and MWIR HSI for detection, recognition and identification of hard-to-detect targets in environments cluttered with background noise is especially critical. HSI sensors provide image data containing both spatial and spectral information. The spectral information offers the additional modality to address such detection tasks that are unachievable by spatial information alone. The spectral information of an HSI stems from the fact that the amount of radiation reflected, absorbed, or emitted - i.e., the radiance - varies with wavelength. HSI sensors measure the radiance of the materials within each image pixel area over a very large number of contiguous spectral wavelength bands.

It is the objective of this program to explore and develop MWIR and LWIR HSI technologies for the detection, acquisition and tracking of a UAV or UAVs during counter UAV surveillance that cannot otherwise be detected using more conventional imaging or radar. The goal is to perform an exploration and investigation of both MWIR and LWIR hyperspectral signatures of UAVs against various environmental backgrounds, suchas sky backgrounds both in day time and night time, to design an effective detection and tracking algorithm with high probability of detection and low probability of false alarm at a range up to 10 km. The detection and identification algorithm should be combined with system designs that employ either innovative sensors or commercial off-the-shelf (COTS) systems. The result should be an effective UAV detection and identification algorithm based on MWIR and LWIR HSI systems with probability of detection more than 90% and probability of false alarm less than 10% at the detection range up to 10 km even at the presence of common atmospheric obscurants, such as fog, clouds and aerosols, where atmospheric obscurants can reduce the visible transmission coefficient at detection distance down to less than 10% relative to that in vacuum. The identification algorithm of the system should incorporate a library of UAVs that will keep pace with those that are available.

PHASE I: Design, document and demonstrate feasibility of a detection and tracking algorithm based on the combined LWIR and MWIR HSI systems of the developer’s choice that meet or exceed the requirements specified in the Description. Identify the technical risk elements in the detection and tracking algorithm design and provide viable risk mitigation strategies. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Construct, develop, and demonstrate a combined HSI system with the associated detection/tracking algorithm based on the design from Phase I. Conduct quantitative measurements and analysis of the system prototype and assess system performance against the stated requirements. Prepare a report that summarizes the experimental evaluation and validation of the performance characteristics of the developed system.

PHASE III: Fully develop and transition the technology and methodology based on the research and development results developed during Phase II for DOD applications in the areas of UAV detection and identification, and other anomaly surveillance and reconnaissance applications.Commercialize the detection and identification technology for commercial aviation enhanced vision, chemicals/explosives sensing, detection of toxic gases, environmental monitoring, and non-invasive health monitoring and sensing.

KEYWORDS: Unmanned Aerial Vehicles, UAV, Image, Detection, Identification, Hyperspectral, Multi-Spectral


1. Pringle, C. “US Marines to Test Drone-Killing Laser Weapons.” Defense News, Sightline Media Group, June 19, 2019. . 2. Williams, R. “Tokyo Police are Using Drones with Nets to Catch Other Drones.” The Telegraph, 11 December 2015. 3. Liptak, A. “A US Ally Shot Down a $200 Drone with a $3 Million Patriot Missile.” The Verge, March 16, 2017.

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