[2305.02064] Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries

[2305.02064] Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries: 

In this article, we introduce a novel algorithm for efficient near-field synthetic aperture radar (SAR) imaging for irregular scanning geometries. With the emergence of fifth-generation (5G) millimeter-wave (mmWave) devices, near-field SAR imaging is no longer confined to laboratory environments. Recent advances in positioning technology have attracted significant interest for a diverse set of new applications in mmWave imaging. However, many use cases, such as automotive-mounted SAR imaging, unmanned aerial vehicle (UAV) imaging, and freehand imaging with smartphones, are constrained to irregular scanning geometries. Whereas traditional near-field SAR imaging systems and quick personnel security (QPS) scanners employ highly precise motion controllers to create ideal synthetic arrays, emerging applications, mentioned previously, inherently cannot achieve such ideal positioning. In addition, many Internet of Things (IoT) and 5G applications impose strict size and computational complexity limitations that must be considered for edge mmWave imaging technology. In this study, we propose a novel algorithm to leverage the advantages of non-cooperative SAR scanning patterns, small form-factor multiple-input multiple-output (MIMO) radars, and efficient monostatic planar image reconstruction algorithms. We propose a framework to mathematically decompose arbitrary and irregular sampling geometries and a joint solution to mitigate multistatic array imaging artifacts. The proposed algorithm is validated through simulations and an empirical study of arbitrary scanning scenarios. Our algorithm achieves high-resolution and high-efficiency near-field MIMO-SAR imaging, and is an elegant solution to computationally constrained irregularly sampled imaging problems.

Comments:	Accepted to IEEE Access
Subjects:	Signal Processing (eess.SP); Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2305.02064 [eess.SP]
	(or arXiv:2305.02064v1 [eess.SP] for this version)
	https://doi.org/10.48550/arXiv.2305.02064

Journal reference:	IEEE Access, vol. 10, pp. 10283-10294, 2022
Related DOI:	https://doi.org/10.1109/ACCESS.2022.3145370

Figure 4. System design for 3-D scanner with radar mounted on planar x-y rails and target mounted on a linear z rail. The TI radar, data capture card, and mechanical scanner are controlled by MATLAB via USB serial interface.

 

Summary in technical detail "Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries" by Josiah Smith, Murat Torlak Department of Electrical and Computer Engineering
The University of Texas at Dallas Richardson, TX 75080 josiah.smith@utdallas.edu

https://arxiv.org/pdf/2305.02064

The paper "Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries" presents a novel approach for efficient three-dimensional (3-D) imaging of near-field multi-input multi-output synthetic aperture radar (MIMO-SAR) data. The proposed approach is designed to handle irregular scanning geometries, which can occur in practical applications of MIMO-SAR imaging.

The proposed approach consists of two main steps: 

(1) a fast beamforming algorithm that generates focused images from the raw MIMO-SAR data, and

(2) a 3-D image reconstruction algorithm that produces a high-resolution 3-D image from the focused images.

The fast beamforming algorithm is based on a weighted delay-and-sum (DAS) approach, which is used to generate focused images from the raw MIMO-SAR data. The DAS approach involves delaying and summing the received signals from each antenna element, which produces a focused image of the scene. The weights used in the DAS approach are optimized to minimize the sidelobe levels of the resulting image.

The 3-D image reconstruction algorithm is based on a compressed sensing (CS) framework, which is used to reconstruct a high-resolution 3-D image from the focused images generated by the beamforming algorithm. The CS framework involves solving an optimization problem that balances the sparsity of the 3-D image in a transform domain with the fidelity of the observed data.

To handle the irregular scanning geometries, the proposed approach uses a spatial Fourier transform to map the irregularly sampled data to a regular grid. The Fourier transform is applied separately to each antenna element, which allows for efficient processing of large datasets. The mapped data is then used as input to the fast beamforming algorithm.

The proposed approach is evaluated using both simulated and real-world MIMO-SAR data with irregular scanning geometries. The results show that the proposed approach is able to produce high-quality 3-D images with improved resolution compared to existing approaches. Additionally, the proposed approach is shown to be computationally efficient, with processing times that are orders of magnitude faster than existing CS-based approaches.

In summary, the paper proposes a novel approach for efficient 3-D imaging of near-field MIMO-SAR data with irregular scanning geometries. The proposed approach combines a fast beamforming algorithm with a CS-based 3-D image reconstruction algorithm, and uses a spatial Fourier transform to handle irregular sampling patterns. The results show that the proposed approach is effective in producing high-quality 3-D images with improved resolution, while also being computationally efficient.