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Planetary Instrument Concepts for the Advancement of Solar System Observations

An ultra-compact midwave hyperspectral framing camera

Completed Technology Project
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Project Description

An ultra-compact midwave hyperspectral framing camera
Mapping spectrometers, or hyperspectral imagers, enable remote identification of minerals, biomarkers, and the composition of atmospheres; such tools have been successfully employed in several NASA applications. Traditionally, imaging spectroscopy systems are extremely complex instruments, often with significant Size, Weight, and Power (SWaP) requirements. Under a NASA STTR program, Nanohmics and the University of Maryland demonstrated a VIS/NIR chip-scale hyperspectral framing camera technology based on computational speckle-based spectroscopy techniques—this PICASSO proposal seeks to extend this technology to the midwave infrared (MWIR, about 3-5 microns) where many important molecular vibrational bands and mineral spectral signatures are present. Instruments operating in the MWIR spectral region face several challenges that are mitigated by our chip-scale spectroscopy technique: because the entire spectroscopic element is 1 mm thick and sits less than 1 mm from the image sensor, it will be integrated into the same dewar as the image sensor, adding only a small passive thermal load to the system. This simplification will result in cascading savings in SWaP and cost, allowing it to be integrated even with CubeSats and rovers. The program goal is the development and subsequent demonstration of a MWIR hyperspectral framing camera with a chip-scale spectral element. To achieve this goal, we will: (1) Refine the needed spectral resolving power, spatial resolution, etendue, integration times, and other optics design parameters required to accomplish high priority measurements derived from the Planetary Decadal Survey (PDS), including comet and small body flyby and sample return, in situ Lunar resource prospecting, determining the habitability of Mars and Ocean Worlds, or a Uranus probe. (2) Determine the optimized spectroscopic element design parameters required to achieve the desired spectral and imaging performance for selected candidate missions through simulation and modeling. (3) Design, fabricate, and assemble a functioning chip-scale MWIR spectral imaging element using microfabrication facilities. (4) Integrate the chip with an existing sensor and characterize spectral, radiometric, and imaging performance. The MWIR hyperspectral framing camera is currently TRL 2+. PICASSO funds will mature the hyperspectral technology to TRL 4 for infusion into the MatISSE program. Successful maturation through MatISSE funding will strategically position this technology for integration onto payloads on multiple planned planetary missions, including CubeSats, rovers, and larger missions. Our team from academia and industry has extensive NASA ties and will leverage prior NASA STTR investments to enable new science by significantly improving instrument measurement capabilities for planetary science missions including Discovery, New Frontiers, Mars Exploration opportunities. Several elements of NASA’s PSD will be addressed by our technology: 1) Understand the processes that determine habitability in the solar system: enables characterization of geothermal activity through multi-frame hyperspectral movies; determines chemical composition of deposits on planets, moons, and small bodies; and help determine water and hydrate abundance and cycles. 2) Identify and investigate past or present habitable environments and determine if there is/has been life elsewhere in the solar system: spectral mapping of organics and biomarkers at either reconnaissance distances or rover distances provides candidate sites for further investigation. 3) Explore the space environment to identify hazards and resources for human presence: spectral signatures of hydrates and ice provide information about where water and other in situ resources can be acquired. 4) Sample return missions from moon, small bodies, and Mars: Spectral imaging of prospective sample collection regions can optimize choices to ensure best collection choices. More »

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