RT&L FOCUS AREA(S): Hypersonics TECHNOLOGY AREA(S): Sensors; Weapons 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 section 3.5 of 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: Develop a real-time computational pipeline that meets the demanding latency and throughput requirements for real-time single-shot artificial intelligence (AI) enhanced accurate wavefront sensing in imaging and directed energy applications. DESCRIPTION: The phase distortions caused by the propagation of coherent light through deep layers of atmospheric turbulence create fundamental physical limitations for the problems of both optical imaging and directed energy (DE) in long-range air-to-ground and ground-to-air applications. As coherent light passes through many layers of atmospheric turbulence, the wavefront is distorted in a way so that a traditionally formed image is blurred with a space-variant distortion. Emerging methods in digital holographic (DH) imaged together with the fusion of advanced AI methods with advanced physics-based sensor models offer the possibility of recovering a model of the propagation distortion, so that the wavefront can be corrected. However, in order for these technologies to have impact, novel algorithms and integrated software/hardware systems must be created and implemented that allow for real-time closed-loop recovery and correction of optical wavefront distortion from a single-shot of data. This SBIR topic looks to develop a volumetric wavefront sensing (WFS) computational pipeline that meets the latency, throughput, and accuracy requirements required for integration into a real-time imaging (i.e., Intelligence, Surveillance, and Reconnaissance (ISR)) or directed energy system. The end goal of this SBIR topic is to design (Phase I) and demonstrate (Phase II) a volumetric WFS prototype computational pipeline that can operate in the presence of extended non-cooperative targets and distributed-volume aberrations. The Phase I effort shall develop the integrated theoretical algorithms, software, and computational hardware systems required to meet the demanding throughput and latency requirements of closed-loop volumetric-wavefront sensing for both imaging and DE applications. The Phase II effort shall then implement these approaches in a prototype demonstration system to achieve the target performance on a scaled-laboratory optical system. The outcomes of the proposed work are: 1) Integrated theoretical algorithms, software and computational hardware systems that can meet the throughput and latency requirements of closed-loop volumetric-wavefront sensing for both imaging and DE applications; and 2) A demonstrated prototype system, which can achieve the specified target performance on a scaled-laboratory optical system. The Phase I effort will not require access to classified information. If needed, data of the same level of complexity as secured data will be provided to support Phase I work. The Phase II effort may require secure access; if so, SSP will process the DD254 to support the contractor for personnel and facility certification for secure access. Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DCSA and SSP in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract. PHASE I: Create theoretical methods for integrating AI with coherent optical sensor models for accurate estimation of volumetric phase distortion in long-range imaging and DE applications. Perform feasibility analysis of software/hardware pipeline for real-time implementation meeting latency and throughput requirements. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II. PHASE II: Build an integrated algorithmic, software, and hardware prototype system that performs low latency and high throughput computation of accurate wavefront parameters for compensation in imaging and DE applications. Demonstrate real-time system performance on a scaled-laboratory optical system. It is probable that the work under this effort will be classified under Phase II (see Description section for details). PHASE III DUAL USE APPLICATIONS: Developing a real-time computational pipeline with real-time single-shot AI enhanced accurate wavefront sensing can be applied to other systems associated with long-range missions at increased speeds that utilize imaging and directed energy applications. REFERENCES: Pellizzari, Casey; Spencer, Mark; and Bouman, Charles. Coherent-Image Reconstruction Using Convolutional Neural Networks. Optical Society of America (OSA) Imaging and Applied Optics Congress, 24-27 June 2019. https://www.osapublishing.org/viewmedia.cfm?uri=MATH-2019-MTu4D.4&seq=0 Pellizzari, Casey; Spencer, Mark; and Bouman, Charles. Imaging Through Distributed-Volume Aberrations using Single-Shot Digital Holography. The Journal of the Optical Society of America A (JOSA-A), 36.2, 1 February 2019, pp. A20-A23. https://engineering.purdue.edu/~bouman/publications/orig-pdf/josaa-36-2-A20.pdf Pellizzari, Casey; Spencer, Mark; and Bouman, Charles. Demonstration of single-shot digital holography using a Bayesian framework. The Journal of the Optical Society of America A (JOSA-A), 35.1, 1 January 2018, pp. 103-107. https://engineering.purdue.edu/~bouman/publications/orig-pdf/josaa8.pdf Pellizzari, Casey; Spencer, Mark; and Bouman, Charles. Phase-Error Estimation and Image Reconstruction from Digital-Holography Data using a Bayesian Framework. The Journal of the Optical Society of America A (JOSA), 34.9, 1 September 2017, pp. 1659-1669. https://engineering.purdue.edu/~bouman/publications/orig-pdf/josa7.pdf Pellizzari, Casey; Trahan, Russell; Zhou, Hangying; Williams, Skip; Williams, Stacie; Nemati, Bijan; Shao, Michael; and Bouman, Charles. Optically-Coherent Image Formation and Denoising Using Plug and Play Inversion Framework. Applied Optics 56.16, 1 June 2017, pp. 4735-4744. https://engineering.purdue.edu/~bouman/publications/orig-pdf/AO3.pdf
