TR2020-114

Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar


    •  Liu, D., Chen, S., Boufounos, P.T., "Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2020.
      BibTeX TR2020-114 PDF Video
      • @inproceedings{Liu2020jul,
      • author = {Liu, Dehong and Chen, Siheng and Boufounos, Petros T.},
      • title = {Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar},
      • booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-114}
      • }
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  • Research Areas:

    Computational Sensing, Optimization, Signal Processing

The performance of synthetic aperture radar degrades when its moving platform is perturbed with unknown position errors or received signals are interfered by strong random noise. Therefore, it is desirable to perform robust imaging with noisy radar echoes even under large position perturbations. In this paper, we propose a graph-based denoising method, which regularizes both the smoothness in the graph domain and the sparse gradients in the time domain. Different from previous GSP-based methods, our graph model is built in the radar signal domain instead of the image domain, so that we can jointly estimate position perturbations of the radar platform and denoise the received signals, providing focused imaging results. Simulation results demonstrate that our method improves the radar imaging quality from 13.3dB provided by coherence analysis to 21.6dB in terms of PSNR.

 

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