Online Sparse Synthetic Aperture Radar Imaging

TL;DR

This paper introduces an online SAR imaging algorithm that efficiently reconstructs scenes incrementally with limited data, reducing memory use and enabling real-time target recognition onboard autonomous drones.

Contribution

It presents the Online FISTA algorithm, a novel online sparse reconstruction method that updates images recursively without storing all data, improving efficiency for drone-based SAR applications.

Findings

Reduces memory requirements for SAR image reconstruction.

Enables real-time automatic target recognition onboard drones.

Improves integration of data collection and processing.

Abstract

With modern defense applications increasingly relying on inexpensive, autonomous drones, lies the major challenge of designing computationally and memory-efficient onboard algorithms to fulfill mission objectives. This challenge is particularly significant in Synthetic Aperture Radar (SAR), where large volumes of data must be collected and processed for downstream tasks. We propose an online reconstruction method, the Online Fast Iterative Shrinkage-Thresholding Algorithm (Online FISTA), which incrementally reconstructs a scene with limited data through sparse coding. Rather than requiring storage of all received signal data, the algorithm recursively updates storage matrices for each iteration, greatly reducing memory demands. Online SAR image reconstruction facilitates more complex downstream tasks, such as Automatic Target Recognition (ATR), in an online manner, resulting in a more versatile and integrated framework compared to existing post-collection reconstruction and ATR approaches.