Online Sparse Synthetic Aperture Radar Imaging
This work addresses the problem of efficient SAR processing for defense applications using drones, but it is incremental as it builds on existing sparse coding methods.
The paper tackles the challenge of computationally and memory-efficient onboard algorithms for Synthetic Aperture Radar (SAR) imaging on drones by proposing an online reconstruction method called Online FISTA, which incrementally reconstructs scenes with limited data through sparse coding, reducing memory demands and enabling online downstream tasks like Automatic Target Recognition.
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.