Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery

TL;DR

This paper introduces novel sparse recovery algorithms for 3D debris localization using rotating point spread function imagery, enabling efficient and stable identification of space debris in a single snapshot.

Contribution

The paper presents new non-convex optimization algorithms specifically designed for large-scale sparse 3D inverse problems in space debris imaging.

Findings

Algorithms demonstrate high efficiency in simulations

Methods show stability in noisy conditions

Effective 3D localization of space debris achieved

Abstract

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.