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Online Sparse Synthetic Aperture Radar Imaging

Conor Flynn, Radoslav Ivanov, Birsen Yazici

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.

Online Sparse Synthetic Aperture Radar Imaging

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.
Paper Structure (12 sections, 18 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 12 sections, 18 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Radar Imaging
  3. Sparse SAR
  4. Dictionary Model
  5. SAR Image Reconstruction as a Sparse Signal Recovery Problem
  6. Online FISTA
  7. Memory Usage
  8. Exact Recovery Guarantee
  9. Experiments
  10. Experiment Setup
  11. Results
  12. Conclusion

Figures (6)

  • Figure 1: Example of non-vectorized edgelet based atoms.
  • Figure 2: Experiment sample scenes.
  • Figure 3: Large coefficients ($c>2\times 10^{-2}$) compared to slow-time over each scene.
  • Figure 4: SNR Gain vs slow-time comparison between Online FISTA and BP. Gain is measured in the difference between the SNR of Online FISTA and BP at each slow-time instance.
  • Figure 5: Partial reconstruction comparison between Online FISTA and BP.
  • ...and 1 more figures