Compressive Sensing Algorithm Development for Time-Series Photometric Measurements

We propose to develop and characterize a new approach for simultaneous data acquisition and compression for time-series photometric measurements based on modern compressive sensing (CS) techniques. We will analyze the effects and limitations of compressive sensing on time-series photometric measurements - although this technique may be used on a much broader array of remote sensing applications. We will develop a proof-of-concept for a CS based architecture for capturing time-series photometric measurements using simulated and observational gravitational microlensing data sets.

CS holds the potential to reduce on-orbit data pre-processing and transmission volume. Consequently, these techniques may facilitate a significant increase in the science capabilities of small-to-medium sized, energy and communications bandwidth-limited spacecraft. This technique will enable us to reduce on-spacecraft data storage, computing and communications bandwidth, and spacecraft power. Achieving higher observing cadence is also great potential of CS, due to its single measurement acquisition system that reduces the typical bottlenecks that limit imaging rapidity – during acquisition.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.