High-resolution Sparse Radar Imaging

I. Compressed Sensing Based Sparse Imaging

The sparse SAR imaging technology using compressed sensing has been developed for super-resolution, feature enhancement, etc. In our previous study, a comprehensive review on sparse synthetic aperture radar imaging from compressed sensing and machine learning is made in [1], interferometric ISAR for 3D imaging [2], sparse aperture imaging and scaling for moving targets [3] and maneuvering targets [4] are surveyed using compressed sensing techniques.

Fig.1. ISAR imaging of sparse aperture using sparse algorithms (sparse sampling ratio is 1/4). (a) CRS, (b) CGS. (the first column: MP, the second column: L1-norm, the third column: BCS, the forth column: structured BCS).

Fig. 2. 3D geometry estimation using the proposed algorithm. RMS: (a) 3/4 data amount; (b) 1/2 data amount. GMS: (c) 3/4 data amount; (d) 1/2 data amount.

Related Publications

[1] Gang Xu*, Bangjie Zhang, Hanweng Yu, Jianlai Chen, Mengdao Xing and Wei Hong, "Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends," IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 4, pp. 32-69, Dec. 2022, doi: 10.1109/MGRS.2022.3218801.

[2] Gang Xu*, Mengdao Xing, Xianggen Xia, Lei Zhang, Qianqian Chen and Zheng Bao, "3D Geometry and Motion Estimations of Maneuvering Targets for Interferometric ISAR With Sparse Aperture," IEEE Transactions on Image Processing, vol. 25, no. 5, pp. 2005-2020, May 2016, doi: 10.1109/TIP.2016.2535362.

[3] Gang Xu*, Mengdao Xing, Xianggen Xia, Qianqian Chen, Lei Zhang and Zheng Bao, "High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling With Sparse Aperture," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 4010-4027, Aug. 2015, doi: 10.1109/JSTARS.2015.2439266.

[4] Gang Xu*, Mengdao Xing, Lei Zhang, Jia Duan, Qianqian Chen and Zheng Bao, "Sparse Apertures ISAR Imaging and Scaling for Maneuvering Targets," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2942-2956, July 2014, doi: 10.1109/JSTARS.2014.2315630.

II. Sparsity-driven Based Autofocusing

To solve the inevitable model errors including MTRC and phase error modulation, sparsity-based autofocus algorithms are proposed by integrating the motion compensation during SAR/ISAR imaging. In our previous study, ISAR imaging and scaling for effective MTRC correction and sparse aperture synthesis is proposed using Bayesian compressed sensing (BCS) [1]. Further, sparse imaging and scaling for maneuvering target is proposed using quasi-Newton method [2] and parametric sparse Bayesian learning (SBL) [3].

Fig.1. Sparsity-based autofocusing of ISAR imaging under three different types of phase error (low order, high order and random).

Fig.2. Imaging of maneuvering targets at different SNR.

Related Publications

[1] Gang Xu*, Mengdao Xing, Lei Zhang, Yabo Liu and Yachao Li, "Bayesian Inverse Synthetic Aperture Radar Imaging," IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 6, pp. 1150-1154, Nov. 2011, doi: 10.1109/LGRS.2011.2158797.

[2] Gang Xu*, Lei Yang, Guoan Bi, and Mengdao Xing, “Maneuvering Target Imaging and Scaling by Using Sparse Inverse Synthetic Aperture,” Signal Processing, vol.149, no.11, pp.149-159, 2017.

[3] Gang Xu*, Lei Yang, Guoan Bi and Mengdao Xing, "Enhanced ISAR Imaging and Motion Estimation With Parametric and Dynamic Sparse Bayesian Learning," IEEE Transactions on Computational Imaging, vol. 3, no. 4, pp. 940-952, Dec. 2017, doi: 10.1109/TCI.2017.2750330.

Ⅲ. Structured Low-rank Based Sparse Imaging

The low-rank structure embedded in high dimensional radar data is exploited and applied for gridless compressed sensing. The structured low-rank property of Hankel matrix is used in [1] and combined with sparsity in [2] for sparse ISAR imaging.

Fig.1. ISAR imaging of sparse aperture using sparse algorithms (sparse sampling ratio is 1/4). (a) CRS, (b) CGS. (the first column: MP, the second column: L1-norm, the third column: BCS, the forth column: structured BCS).

Related Publications

[1] Gang Xu*, Bangjie Zhang, Jianlai Chen, Fan Wu, Jialian Sheng and Wei Hong, "Sparse Inverse Synthetic Aperture Radar Imaging Using Structured Low-Rank Method," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12, 2022, Art no. 5213712, doi: 10.1109/TGRS.2021.3118083.

[2] Gang Xu*, Bangjie Zhang, Jianlai Chen and Wei Hong, "Structured Low-Rank and Sparse Method for ISAR Imaging With 2-D Compressive Sampling," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5239014, doi: 10.1109/TGRS.2022.3221971.

Ⅳ. Feature-enhanced Sparse 3D SAR Imaging

As an important tool for topographic mapping, forest parameter estimation, urban buildings modeling and etc, 3D SAR imaging using interferometric SAR [1] and array SAR tomography [2] are explored.

Fig.1. Array SAR tomography and imaging results of single building by the proposed method.

Fig. 2. Filtered interferometric phase. (a) Original (residue numbers 56819). (b) Lee filter (residue numbers 6161). (c) WInPF filter (residue numbers 5784). (d) Region growing method (residue numbers 2189). (e) B-InSAR-IF algorithm (residue numbers 3773).

Related Publications

[1] Gang Xu*, Mengdao Xing, Xianggen Xia, Lei Zhang, Yanyang Liu, Zheng Bao, "Sparse Regularization of Interferometric Phase and Amplitude for InSAR Image Formation Based on Bayesian Representation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2123-2136, April 2015, doi: 10.1109/TGRS.2014.2355592.

[2] Bangjie Zhang, Gang Xu*, Hanwen Yu, Hui Wang, Hao Pei and Wei Hong, "Array 3-D SAR Tomography Using Robust Gridless Compressed Sensing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023, Art no. 5205013, doi: 10.1109/TGRS.2023.3259980.