Development of ultra-compact near-IR hyperspectral imagers for the exploration of the Solar System using acousto-optical filtering technique.
IAS, as a space laboratory, is developing new instrument concepts that will meet the requirements of future space missions. As part of the multi-scale characterization of Solar System objects, hyperspectral imaging in the visible / near-IR allows to characterize the surface composition and thus to go back to the different processes that have taken place. One of the major challenges linked to this technique consists in miniaturizing the instruments in order, in particular, to make them accessible to in situ missions. The R&T ExoCam project aims to develop a new instrumental concept responding to this demand by relying on the technique of acousto-optical filtering.
The acousto-optical filters (AOTF, Acousto-Optic Tunable Filter) are compact systems of light scattering in the visible / near-IR range electrically controlled, and with a very high efficiency. The filtering carried out by the acousto-optical filter also makes it possible to preserve the spatial information. The addition of an AOTF thus makes it possible to transform an imaging system into a hyperspectral imaging system with a moderate volume / mass increase. R&T activities carried out at the IAS have notably made it possible to demonstrate the validity of this concept thanks to the development of an instrumental bench on the 1.0-1.7 μm range. The ExoCam project aimed to continue this work by extending the study spectral range to the 0.9-3.6 μm range, representative of that desired for a flight instrument (allowing the characterization of most minerals, ice and organic compounds), and demonstrate the feasibility of such an instrument in a mass / volume / power budget compatible with the constraints of a rover / lander.
This concept can also be used for instruments mounted on probes and performing measurements from orbit. This technique is particularly effective in the case of dark objects due to its high filtering efficiency.
Funding : R&T CNES