# Online Sparse Synthetic Aperture Radar Imaging

arXiv: https://arxiv.org/abs/2603.08582 · HTML: https://mlsanity.com/paper/2603.08582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Scalable Online Convolutional Sparse Coding](https://mlsanity.com/paper/1706.06972/) (2017)
- [Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models](https://mlsanity.com/paper/1809.01817/) (2018)
- [The Comparability of Model Fusion to Measured Data in Confuser Rejection](https://mlsanity.com/paper/2505.00836/) (2025)
- [Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm](https://mlsanity.com/paper/1306.5310/) (2013)

## Built on by (most cited first, 1 of 1)

- [Online CS-based SAR Edge-Mapping](https://mlsanity.com/paper/2604.19989/) (2026)

## Abstract (arXiv)

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.