RMIST-Net: Joint Range Migration and Sparse Reconstruction Network for 3-D mmW Imaging

Wang, Mou; Wei, Shunjun; Liang, Jiadian; Zeng, Xiangfeng; Wang, Chen

doi:10.1109/tgrs.2021.3068405

Cited by 39 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, both the simulated and real-measured experiments are performed to validate the effectiveness and efficiency of the proposed method. The comparison baselines include two types of imaging algorithms, i.e., the conventional MF-based imaging methods, including range migration algorithm (RMA), MF in frequency domain [47], and SF holography algorithm (SF Holo) [42]; the network-based reconstruction method, RMIST-Net [43]. Considering the superiority of RMIST-Net over ISTA [13] and CSR-Net [48] has been proven in [43], thus the comparisons with them are not provided here.…”

Section: Methodsmentioning

confidence: 99%

“…Particularly, KFISTA terminates under the condition that a maximum number of iterations exceeds nine times or the variation of historical estimations are under the tolerance (1e −3 ). By default, for RMIST-Net, the number of iterations blocks is set to nine [43], while the proposed LFIST-Net only adopts three updating layers. Even so, it still can be experimentally proved that LFIST-Net outperforms RMIST-Net quite a margin in the following parts.…”

Section: A Numerical Investigationmentioning

confidence: 99%

“…1) A lightweight model-based deep learning framework (LFIST-Net) is proposed for mmW 3-D holography, which bridges the gap between model-based CS and data-driven deep learning technique. Compared with our previous work [43], the proposed LFIST-Net needs three times fewer updating layers (three layers) than RMIST-Net (nine layers) to achieve similar reconstruction accuracy, which brings a speed improvement. 2) Key parameters of LFIST-Net are automatically learned in an end-to-end manner.…”

mentioning

confidence: 94%

See 2 more Smart Citations

Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography

Wang

Wei

Liang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

Integrating compressed sensing (CS) with millimeter-wave (mmW) holography has shown great potential to achieve lightweight onboard hardware, low sampling ratio, and high-speed sensing. However, conventional CS-driven algorithms are always limited by nontrivial adjusting of parameters and excessive computational cost caused by plenty of iterations. To address this problem, we propose a lightweight model-based deep learning framework (LFIST-Net) for mmW 3-D holography, by combining the interpretability of fast iterative shrinkage-thresholding algorithm (FISTA) and tuning-free merit of data-driven deep neural network. First, the single-frequency (SF) holographic imaging technique is integrated into FISTA, which serves as the sensing kernels, to avoid large-scale matrix multiplications. Subsequently, the kernel-based FISTA (KFISTA) is mapped into layer-fixed and parameter-learnable LFIST-Net, whose weights are relaxed to be layer-varied. The updating of key parameters in LFIST-Net, including step sizes, thresholds, and momentum coefficients, are regularized by soft-plus function to ensure the non-negativity and monotonicity. As for 3-D holography implementation, the "1-D + 2-D" scheme is adopted, where the matched filtering (MF) and well-trained LFIST-Net are used for range focusing and reconstructions of azimuth slices. Without losing efficiency, the range-focused subechoes are processed parallelly in 3-D cube form. Experiments, including both simulated and measured tests based on a commercial mmW radar, prove that LFIST-Net is capable of reconstructing the imaging scene precisely. In particular, in near-field mmW 3-D holography tests, both numerical and visual results demonstrate LFIST-Net yields compelling reconstruction performance while maintaining high computational speed compared with MF-based, conventional CS-driven, and network-based methods. Index Terms-3-D holography, compressed sensing (CS), deep unfolding, fast iterative shrinkage-thresholding algorithm (FISTA), millimeter-wave (mmW) imaging, near-field, sparse reconstruction. I. INTRODUCTIONN EAR-FIELD 3-D millimeter-wave (mmW) holography has gained significant concerns in the microwave theory and techniques society. mmW, possessing the merits of reasonable penetration for most nonmetal materials and no known Manuscript

show abstract

Section: Methodsmentioning

confidence: 99%

Section: A Numerical Investigationmentioning

confidence: 99%

mentioning

confidence: 94%

See 1 more Smart Citation

Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography

Wang

Wei

Liang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…RMIST-Net [31] generates imaging results through multiple iterations based on the theory of matched filtering and compressed sensing. Detailed steps can be found in [31] but will not be detailed here. The echo data can be expressed as Equation (18).…”

Section: Rmist-netmentioning

confidence: 99%

“…Furthermore, the method of using machine learning to build deep neural networks for imaging is also emerging, and many efficient technologies have come out, such as ISTA-Net [28], AMP-Net [29]; however, they are not used for MMW imaging. In the near-field MMW imaging field, Wang et al presented a novel 3-D microwave sparse reconstruction method based on a complex-valued sparse reconstruction network (CSR-Net) [30], a novel range migration kernel-based iterative-shrinkage thresholding network (RMIST-Net) [31], and a lightweight FISTA-Inspired Sparse Reconstruction Network for MMW 3-D Holography [32]. In the aspect of application, a detection and classification algorithm based on the MMW radar and camera fusion is proposed in [33]; Cui et al [34] presented a K-means-based machine learning algorithm for user clustering with MMW system; Ref.…”

Section: Introductionmentioning

confidence: 99%

3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation

et al. 2021

Self Cite

View full text Add to dashboard Cite

Millimeter-wave (MMW) 3-D imaging technology is becoming a research hotspot in the field of safety inspection, intelligent driving, etc., due to its all-day, all-weather, high-resolution and non-destruction feature. Unfortunately, due to the lack of a complete 3-D MMW radar dataset, many urgent theories and algorithms (e.g., imaging, detection, classification, clustering, filtering, and others) cannot be fully verified. To solve this problem, this paper develops an MMW 3-D imaging system and releases a high-resolution 3-D MMW radar dataset for imaging and evaluation, named as 3DRIED. The dataset contains two different types of data patterns, which are the raw echo data and the imaging results, respectively, wherein 81 high-quality raw echo data are presented mainly for near-field safety inspection. These targets cover dangerous metal objects such as knives and guns. Free environments and concealed environments are considered in experiments. Visualization results are presented with corresponding 2-D and 3-D images; the pixels of the 3-D images are 512×512×6. In particular, the presented 3DRIED is generated by the W-band MMW radar with a center frequency of 79GHz, and the theoretical 3-D resolution reaches 2.8 mm × 2.8 mm × 3.75 cm. Notably, 3DRIED has 5 advantages: (1) 3-D raw data and imaging results; (2) high-resolution; (3) different targets; (4) applicability for evaluation and analysis of different post processing. Moreover, the numerical evaluation of high-resolution images with different types of 3-D imaging algorithms, such as range migration algorithm (RMA), compressed sensing algorithm (CSA) and deep neural networks, can be used as baselines. Experimental results reveal that the dataset can be utilized to verify and evaluate the aforementioned algorithms, demonstrating the benefits of the proposed dataset.

show abstract