TECHNOLOGY AREA(S): Information Systems, Sensors, Weapons

ACQUISITION PROGRAM: PEO IWS 2; PEO IWS 3; SEWIP POR; SPADE POR; CESARS FNC

OBJECTIVE: Develop and demonstrate an active EO/IR imaging system that employs joint optimization of multiple laser illumination characteristics (e.g., pulse temporal structure, repetition rate, beam spatial profile, polarization, and quantum statistical composition, etc.) together with advanced processing techniques to enhance operational range in dense maritime fog by a factor at least 10 times greater than that of current active imaging systems.

DESCRIPTION: The U.S. Fleet Forces are often present in congested waterways throughout the world for a variety of humanitarian and military purposes.  EO/IR imaging systems are often employed in such settings to maintain SA as well as for target recognition, tracking, and identification.  However, EO/IR imagery is highly susceptible to degradation caused by scattering from ubiquitous, water-based aerosols.  Imaging through dense fog is the quintessential hard problem, as strong scattering generates a large, uninformative background, while information-carrying ballistic photons are severely attenuated.  The goal of active imaging is to augment target illumination intensity, while selectively detecting returned ballistic photons against extraneous background.

In contrast to passive imaging, active imaging benefits from multiple degrees of freedom that can be controlled for the illumination source to enhance selective detection of the ballistic return, including laser pulse temporal profile, repetition rate, energy, wave front structure, spectral band, polarization characteristics, coherence, orbital angular momentum, photon-statistical properties, and degree of entanglement (quantum or classical).  Conventional temporal gating techniques illuminate the target with a laser pulse and correlate opening of a narrow detection window with the arrival of the ballistic return signal, thereby reducing detection of extraneous background.  Although the ballistic photons must also propagate through the obscuring atmosphere, the point spread function degradation (i.e., blur in the return signal) alone is often less severe than the impact of the extraneous background and can be mitigated through image processing techniques.  Similarly, other active imaging methods impart some unique property to the illumination source to enable extraction of the returned ballistic signal by another variant of correlation.  While the gain in range with conventional temporal gating is substantial, a larger overall improvement could potentially be obtained by combining multiple correlation techniques.  In addition, advanced processing methods, such as convolutional neural network-(CNN) based deep learning, could be combined with conventional processing methods (e.g., dark channel priors or intensity histogram manipulation) to achieve improved range for target recognition, tracking, and ID in fog.

This topic seeks to develop an active EO/IR imaging system with joint optimization of multiple illumination source characteristics and advanced image processing to improve operational range in dense maritime (convective) fog.  Solutions can exploit all or any portion of the electromagnetic spectrum ranging from the ultraviolet (UV) to the far IR, but excluding mm-wave bands.  System designs employing novel sensors or commercial-off-the-shelf (COTS) sensors are both of interest, but the overall design concept should break new ground.  While systems having low size, weight, and power are desirable, the overriding goal of this effort is to achieve a substantial performance improvement of at least 10 times greater in range for target recognition in dense maritime fog compared to existing systems.

PHASE I: Determine feasibility of an active EO/IR system with jointly optimized illumination, sensing, and processing to achieve at least an improvement 10 times greater operationally useful range where a target can be identified compared to existing active imaging systems in the presence of dense maritime fog [4].  Identify key risk elements to achieve this (10X) improvement objective and perform suitable simulations and/or experiments to mitigate these risk factors.  Prepare a publication-quality technical document detailing the system design and performance characteristics.  Develop a Phase II plan.

PHASE II: Construct and demonstrate an active EO/IR imaging system based on the Phase I study.  Conduct quantitative measurements and analysis to verify the purported 10X or greater improvement in operational range.  The experimental validation can be performed in a laboratory environment that simulates the obscuring environment.  Prepare a publication-quality document detailing the Phase II results.

PHASE III DUAL USE APPLICATIONS: Extend the technology to a full system prototype by optimizing the hardware and processing demonstrated in Phase II.  Refine the design to minimize size, weight, and power (SWaP) consumption while introducing mechanical robustness against shock and vibration [5].  Demonstrate the performance of the technology through extensive dockside and possibly shipboard testing.  Provide support in transitioning the technology.  Provide manuals and training materials.

These capabilities will also be relevant to the autonomous vehicles market in the commercial sector.  Most autonomous vehicles being developed rely on ladar to develop a 3D picture of surroundings.  The technology developed under this program should be extended to modify active optical imaging systems so the operation can be extended in presence of fog.

REFERENCES:

1. Tao, QQ., Sun, YX., Shen, F., Xu, Q., Gao, J., and Guo, ZY. “Active imaging with the aids of polarization retrieve in turbid media system.” Optics Communications 2016, Vol. 359, 405. http://www.sciencedirect.com/science/article/pii/S0030401815301899?via%3Dihub

2. van der Laan, J.D., Scrymgeour, D.A., Kemme, S.A., and Dereniak, E.L. “Detection range enhancement using circularly polarized light in scattering environments for infrared wavelengths.” Applied Optics 2015, Vol. 54 (9), 2266-2074. https://www.osapublishing.org/ao/abstract.cfm?uri=ao-54-9-2266&origin=search

3. Riviere, N., Ceolato, R., and Hespel, L. “Active imaging systems to see through adverse conditions: Light-scattering based models and experimental validation.” Journal of Quantitative Spectroscopy & Radiative Transfer 2014. Vol. 146, p. 431-443. http://www.sciencedirect.com/science/article/pii/S0022407314002027

4. Hanafy, M.E., Roggemann, M.C., and Guney, D.O. “Detailed effects of scattering and absorption by haze and aerosols in the atmosphere on the average point spread function of an imaging system.” J. Opt. Soc. Am. A 31(6), 1312–1319 (2014).

5. MIL-STD-810G. “Department of Defense Test Method Standard: Environmental Engineering Considerations and Laboratory Tests.” 31 October 2008.  http://everyspec.com/MIL-STD/MIL-STD-0800-0899/MIL-STD-810G_12306/

KEYWORDS: Active Imaging; LIDAR; LADAR; Fog; Electro-optical; Infrared; Polarization; Multi-spectral; Sensor Fusion; Autonomous; Real-time; Advanced Processing; Intelligence; Surveillance; Reconnaissance; Situational Awareness