Description:
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy;Integrated Sensing and Cyber
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
OBJECTIVE: Investigate and prototype passive imager and laser beam scanners for a Passively Augmented LiDAR (PAL) architecture that can be used for autonomously detecting, recognizing, and identifying objects that may be obscured by background clutter. The USAF needs improved munitions and other airborne system sensing and target discrimination capabilities to perform against contested landscapes and deception techniques. Using this imaging architecture, USAF systems are expected to be able to precisely detect, recognize, and identify hidden targets on the battlefield with high confidence. The PAL architecture is based on a hybrid system consisting of a multi-kilometer range LiDAR and a passive imager. The goal of the hybrid system is for the active and passive sensors to complement one another by producing a sensor having the strengths of both, but not limited by the weaknesses of either individual system. To that end the passive imager provides high frame rate, high resolution, wide Field of Regard (FOR) imaging. The passive imager will be capable of producing image quality sufficient for target ID through shape-based template matching. In the event there are obscured targets, and the system is unable to ID targets, the passive imagery will be used for anomaly detection indicating the potential presence of hidden targets. The anomaly coordinates will be transferred to the LiDAR system for detailed interrogation over a narrow region. Although various types of passive imagers could be employed, we anticipate the initial imager will be a longwave passive polarimetric imager. It will be desirable for the LiDAR system to incorporate advanced material identification modalities including multiple spectral wavelengths and/or optical polarization. The LiDAR may be a conventional 3D imaging LiDAR or a spectropolarimetric LiDAR. In both cases the LiDAR will be capable of providing 3D imagery of the narrow FOR. For the spectropolarimetric LiDAR, the system will also provide the spectropolarimetric properties of objects in the narrow FOR. LiDAR imaging technology does not exist in any current US weapon seeker in inventory. Therefore, state-of-the-art imaging seekers do not include lidar capabilities such as 3D point cloud imaging, foliage penetration, and active spectropolarimetric material classification. The PAL concept overcomes the primary limitation of lidar imaging: scanning large areas in short time durations. However, the PAL concept of quickly scanning small regions within a large FOR is not conventional for lidar systems and requires a novel scanning system to enable lidar on weapon seekers for the first time.
DESCRIPTION: In depth investigation of a PAL system is needed in order to more completely understand the system trade-offs necessary to optimize the ability to detect and identify hidden targets. A compelling PAL system for Air Force applications in remote sensing may utilize a high frame rate passive imager to rapidly scan a large field of view, detect potential regions of interest, and pass location data to a LiDAR imager to obtain spatial, spectral, and/or spectropolarimetric information on the region of interest. Other than target identification in a battlefield, this technology may be useful in geologic, urban, or agricultural aerial surveys. It can also be applied for military or non-military search and rescue missions where hidden targets are commonly involved. In the notional system, the passive imager may be able to detect/ID unobstructed targets as it identifies scene anomalies, then cues the higher resolution foliage penetrating LiDAR to scan small regions at the anomalies and perform more advance material identification. Images from passive systems can contain a mixture of multiple potential surfaces of interest on each image pixel due to finite pixel sizes, finite fields of view, and large imaging distances. This adds to the complexity of target detection and the need for a more sophisticated investigation for a potential passive imager on the PAL system. Therefore, a passive imager may need to employ unique anomaly detection (AD) methods that can include contrast enhancement, global RX detectors, and automatic thresholding to successfully analyze and detect anomalies within a complex heterogenous image. Due to the time constraints in a contested battlefield, the techniques for AD of a passive imager may also need to rank high anomaly regions and flawlessly cue the LiDAR system to scan these regions by priority to quickly enable target identification. Scene anomalies detected by a passive imager may include manmade objects obstructed by natural foliage, which may be missed by an unpolarized or non-multispectral imaging system. Therefore, having both a passive imager and a LiDAR in a system such as PAL is expected to result in higher detection rates of hidden targets. Additionally, the LiDAR system may make use of more advanced operational modes including but not limited to simultaneous operation at multiple wavelengths, and/or sensitivity to optical polarization. Polarization depends on a variety of factors including object geometry, surface material, and observation angles. At one extreme, vegetation tends to have a weak polarization signal compared to manmade objects because of surface roughness and nonuniform composition. On the other hand, manmade objects with flat surfaces and uniform composition can lead to a higher degree of polarization. Polarimetric signatures of numerous materials are described in pBRDF databases.
PHASE I: Investigate literature for background information on a PAL system and perform system design and analysis. This includes Field of View (FOV) steering for the passive imager and LiDAR scanners. It is desired to understand the complex system trade-offs involved in incorporating a passive imaging system with a LiDAR scanner; additionally, understanding the potential performance enhancements enabled by advanced LiDAR including multiple simultaneous spectral wavelengths and/or optical polarization sensitivity is desired. Study should identify scanner designs for phase II.
PHASE II: Procure hardware to build and characterize prototype scanning systems (1-3 from above) for passively augmented LiDAR system. This includes the development and delivery of hardware. It is desirable that the prototype system be capable of operation outdoors to enable field data collection, but it would be acceptable to develop a compelling tabletop system enabling indoor data collection. If the system is limited to indoor operation, then a path to outdoor operation should be clearly defined. Scan imaging frame rate, field of view, and operational range are metrics of interest.
PHASE III DUAL USE APPLICATIONS: Develop full system prototype and demonstrate in relevant field environment. Mature the prototype PAL system to perform real-time LiDAR scanning and data acquisition. The completed system shall locate anomalies and cue the novel LiDAR scanner to interrogate small regions. The system shall consist of government furnished or procured laser, detector, and infrared imaging components, completely integrated with the scanner technology developed in Phase II. The fully integrated PAL system will be tested outdoors for foliage penetration and material classification. System performance metrics include frame rate, laser scan rate, power requirements, maximum range, and processing time.
REFERENCES:
1. Hybrid passive polarimetric imager and lidar combination for material classification, Jarrod P. Brown, Rodney G. Roberts, Darrell C. Card, Christian L. Saludez, and Christian K. Keyser, Opt. Eng. Aug. 2020;
2. Detection of Hidden Objects Using Passive Polarimetric Infrared Imaging, J. Brown, D. Card, C. Welsh, C. Saludez, C. Keyser, R. Roberts, " IEEE Transactions on Geoscience and Remote Sensing, submitted Aug. 2019;
3. LADAR System and Algorithm Design for Spectropolarimetric Scene Characterization, Richard K. Martin, Christian Keyser, Luke Ausley, and Michael Steinke, IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 3735-3746, July 2018;
4. "Single-Pulse Mueller Matrix LiDAR Polarimeter: Modeling and Demonstration, Christian K. Keyser, Richard K. Martin, P. Khanh Nguyen, and Arielle M. Adams, " IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. 3296-3307, June 2019;
5. Single-pulse, Kerr-effect Mueller matrix LiDAR polarimeter, C. Keyser, R. Martin, H. Lopez-Aviles, K. Nguyen, A. Adams, and D. Christiodoulides, Opt. Exp. May 2020.
KEYWORDS: LiDAR; passive imagery; autonomy; anomaly detection; material classification; polarimetric imagery; multispectral imagery
