Operations in Degraded Visual Environments using Millimeter Wave Imagery

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Integrated Sesning and Cyber OBJECTIVE: Develop a millimeter wave imaging system capable of seeing through Degraded Visual Environments (DVE) to address current needs for DVE mitigation within the Defense community. DESCRIPTION: Degraded Visual Environments (DVE) present significant challenges to tactical operations due to lack of situational awareness, which can lead to mission failure and loss of life. Such environments include low light, fog, dust, rain, snow, and other visual obscurants. Imaging in the millimeter wave region of the spectrum has demonstrated great utility for the ability to see through such obscurants with low attenuation, thereby providing navigational and operational cues and consequently a unilateral tactical advantage. [1] Millimeter wave imaging technology has furthermore found application in screening for contraband including person-borne weapons and improvised explosives hidden under clothing. [2] Nonetheless, current millimeter wave imaging technology remains complex, expensive, and high size, weight and power (SWaP); significant reductions in these parameters are required to facilitate greater deployment opportunities and open new applications and markets. To this end, increased leverage of commercial off-the-shelf (COTS) components is needed. The recent proliferation of collision avoidance radar systems within the automotive industry has created a huge market for millimeter wave components, bringing production to scale and driving down prices. Such radar systems are generally able to warn that objects are within a certain detection range, but do not currently identify these objects nor provide intuitive imagery of them. A system consisting of small arrays of these automotive radars in conjunction with rapidly developing artificial intelligence and machine learning technologies creates great potential for low SWaP imaging solutions to DVE with unprecedented capabilities and price points. [3] Leveraging of these recently developed low-cost systems could address current needs for DVE mitigation within the Defense community. PHASE I: Define a system concept and perform a feasibility study. The system should be able to visualize through common degraded visual environments such as low light, fog, dust, rain, snow, and other visual obscurants The system should run at video rate (24 frames/sec or better). The system should have sufficient resolutions to be able to detect and resolve a human shape from a distance of 30 meters or greater. PHASE II: Build a hardware prototype and demonstrate DVE mitigation capability. Construct and demonstrate a working prototype imaging system using the design developed in Phase I. Demonstrate video rate imaging of threats from a distance of 30 meters or greater. Develop artificial intelligence and machine learning technologies to support rapid and robust detection of threats in environment with significantly visual degradation. Deliver the working prototype to the government for further testing. PHASE III DUAL USE APPLICATIONS: Further research and development during Phase III efforts will be directed toward refining the final deployable equipment and procedures. Design modifications based on results from tests conducted during Phase III will be incorporated into the system. Manufacturability specific to Army Concept of Operations (CONOPS) and end-user requirements will be examined. The development of a low-cost solution to imaging in the millimeter wave region has the potential to provide significant benefits to numerous programs within the DOD and will also have application in commercial markets. REFERENCES: 1. R. Appleby, D. A. Robertson, and D. Wikner, Millimeter wave imaging: a historical review, presented at the SPIE Defense + Security, Anaheim, California, United States, May 2017, p. 1018902. doi: 10.1117/12.2262476. 2. S. Yeom, D.-S. Lee, Y. Jang, M.-K. Lee, and S.-W. Jung, Real-time concealed-object detection and recognition with passive millimeter wave imaging, Opt. Express, vol. 20, no. 9, p. 9371, Apr. 2012, doi: 10.1364/OE.20.009371. 3. F. J. Abdu, Y. Zhang, M. Fu, Y. Li, and Z. Deng, Application of Deep Learning on Millimeter-Wave Radar Signals: A Review, Sensors, vol. 21, no. 6, p. 1951, Mar. 2021, doi: 10.3390/s21061951. 4. Paul. H. Lehmann, Michael Jones, and Marc H finger, "Impact of turbulence and degraded visual environment on pilot workload." CEAS Aeronautical Journal, vol 8, no. 3, pp. 413-428, 2017 KEYWORDS: Degraded Visual Environment, DVE, Millimeter Wave, Automotive Radar, Computer Vision, Machine Learning.