A Fast 3-D Chirp Scaling Imaging Technique for Millimeter-Wave Near-Field Imaging

• Published in: IEEE transactions on microwave theory and techniques Vol. 71; no. 2; pp. 827 - 841
• Main Authors: Zhang, Wenrui, Ji, Yicai, Shao, Wenyuan, Lin, Bo, Li, Chao, Fang, Guangyou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Chirp; Chirp scaling (CS); Frequency modulation; Imaging; Imaging techniques; Interpolation; Millimeter waves; millimeter-wave imaging; MIMO communication; multiple-input–multiple-output (MIMO)/single-input–single-output (SISO) radar; Near fields; Phase error; Phased arrays; Radar imaging; Scaling; Signal to noise ratio; Sparse matrices; Wavelengths
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2022.3205926

In this article, a novel 3-D imaging method based on the chirp scaling algorithm (CSA) is proposed for millimeter-wave near-field imaging. We deduce the theoretical formula of CSA applied to multiple-input-multiple-output (MIMO)/single-input-single-output (SISO) array imaging and then analyze the computational complexity and phase error. Compared to the classical Fourier-based algorithm, such as range migration algorithm (RMA), they can replace the redundant interpolation with the phase multiplication. Meanwhile, it can handle the large relative bandwidth system compared with frequency scaling algorithm (FSA). It greatly improves imaging speed and broadens the application scenarios. The rearrangement operation of the MIMO wavenumber spectrum is also accelerated by the sparse matrix multiplication. To further reduce the computation time, we perform the direct rearrangement of the MIMO wavenumber spectrum before phase compensation. It works well but also sacrifices part of the signal-to-noise ratio (SNR), so it may not be suitable for the weakly scattering targets. The simulation data of a 2-D uniform array with the large number of antennas and a published dataset are used to verify the effect of the proposed algorithm. The simulation and experiment results show that the proposed method can effectively improve the computational efficiency.