Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery
This addresses the challenge of locating space debris for space situational awareness, representing an incremental improvement in domain-specific algorithms.
The paper tackled the problem of 3D localization and tracking of space debris using rotating point spread function imagery, developing efficient sparse recovery algorithms based on non-convex optimization, with numerical simulations demonstrating their efficiency and stability.
An optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources in a single snapshot can perform 3D localization and tracking of space debris. When actively illuminated, unresolved space debris, which can be regarded as a swarm of point sources, can scatter a fraction of laser irradiance back into the imaging sensor. Determining the source locations and fluxes is a large-scale sparse 3D inverse problem, for which we have developed efficient and effective algorithms based on sparse recovery using non-convex optimization. Numerical simulations illustrate the efficiency and stability of the algorithms.