148 research outputs found

    A Downward-looking Three-dimensional Imaging Method for Airborne FMCW SAR Based on Array Antennas

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    AbstractWith regard to problems in conventional synthetic aperture radar (SAR), such as imaging distortion, beam limitation and failure in acquiring three-dimensional (3-D) information, a downward-looking 3-D imaging method based on frequency modulated continuous wave (FMCW) and digital beamforming (DBF) technology for airborne SAR is presented in this study. Downward-looking 3-D SAR signal model is established first, followed by introduction of virtual antenna optimization factor and discussion of equivalent-phase-center compensation. Then, compensation method is provided according to reside video phase (RVP) and slope term for FMCW SAR. As multiple receiving antennas are applied to downward-looking 3-D imaging SAR, range cell migration correction (RCMC) turns to be more complex, and corrective measures are proposed. In addition, DBF technology is applied in realizing cross-track resolution. Finally, to validate the proposed method, magnitude of slice, peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR) and two-dimensional (2-D) contour plot of impulse response function (IRF) of point target in three dimensions are demonstrated. Satisfactory performances are shown by simulation results

    ์‹ค์‹œ๊ฐ„ ๊ทผ๊ฑฐ๋ฆฌ ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ MIMO ์—ญํ•ฉ์„ฑ ๊ฐœ๊ตฌ ๋ ˆ์ด๋” ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022. 8. ๋‚จ์ƒ์šฑ.Microwave and millimeter wave (micro/mmW) imaging systems have advantages over other imaging systems in that they have penetration properties over non-metallic structures and non-ionization. However, these systems are commercially applicable in limited areas. Depending on the quality and size of the images, a system can be expensive and images cannot be provided in real-time. To overcome the challenges of the current micro/mmW imaging system, it is critical to suggest a new system concept and prove its potential benefits and hazards by demonstrating the testbed. This dissertation presents Ku1DMIC, a wide-band micro/mmW imaging system using Ku-band and 1D-MIMO array, which can overcome the challenges above. For cost-effective 3D imaging capabilities, Ku1DMIC uses 1D-MIMO array configuration and inverse synthetic aperture radar (ISAR) technique. At the same time, Ku1DMIC supports real-time data acquisition through a system-level design of a seamless interface with frequency modulated continuous wave (FMCW) radar. To show the feasibility of 3D imaging with Ku1DMIC and its real-time capabilities, an accelerated imaging algorithm, 1D-MIMO-ISAR RSA, is proposed and demonstrated. The detailed contributions of the dissertation are as follows. First, this dissertation presents Ku1DMIC โ€“ a Ku-band MIMO frequency-modulated continuous-wave (FMCW) radar experimental platform with real-time 2D near-field imaging capabilities. The proposed system uses Ku-band to cover the wider illumination area given the limited number of antennas and uses a fast ramp and wide-band FMCW waveform for rapid radar data acquisition while providing high-resolution images. The key design aspect behind the platform is stability, reconfigurability, and real-time capabilities, which allows investigating the exploration of the systemโ€™s strengths and weaknesses. To satisfy the design aspect, a digitally assisted platform is proposed and realized based on an AMD-Xilinx UltraScale+ Radio Frequency System on Chip (RFSoC). The experimental investigation for real-time 2D imaging has proved the ability of video-rate imaging at around 60 frames per second. Second, a waveform digital pre-distortion (DPD) method and calibration method are proposed to enhance the image quality. Even if a clean FMCW waveform is generated with the aid of the optimized waveform generator, the signal will inevitably suffer from distortion, especially in the RF subsystem of the platform. In near-field imaging applications, the waveform DPD is not effective at suppressing distortion in wide-band FMCW radar systems. To solve this issue, the LO-DPD architecture and binary search based DPD algorithm are proposed to make the waveform DPD effective in Ku1DMIC. Furthermore, an image-domain optimization correction method is proposed to compensate for the remaining errors that cannot be eliminated by the waveform DPD. For robustness to various unwanted signals such as noise and clutter signals, two regularized least squares problems are applied and compared: the generalized Tikhonov regularization and the total variation (TV) regularization. Through various 2D imaging experiments, it is confirmed that both methods can enhance the image quality by reducing the sidelobe level. Lastly, the research is conducted to realize real-time 3D imaging by applying the ISAR technique to Ku1DMIC. The realization of real-time 3D imaging using 1D-MIMO array configuration is impactful in that this configuration can significantly reduce the costs of the 3D imaging system and enable imaging of moving objects. To this end, the signal model for the 1D-MIMO-ISAR configuration is presented, and then the 1D-MIMO-ISAR range stacking algorithm (RSA) is proposed to accelerate the imaging reconstruction process. The proposed 1D-MIMO-ISAR RSA can reconstruct images within hundreds of milliseconds while maintaining almost the same image quality as the back-projection algorithm, bringing potential use for real-time 3D imaging. It also describes strategies for setting ROI, considering the real-world situations in which objects enter and exit the field of view, and allocating GPU memory. Extensive simulations and experiments have demonstrated the feasibility and potential benefits of 1D-MIMO-IASR configuration and 1D-MIMO-ISAR RSA.๋งˆ์ดํฌ๋กœํŒŒ ๋ฐ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ(micro/mmW) ์˜์ƒํ™” ์‹œ์Šคํ…œ์€ ๋น„๊ธˆ์† ๊ตฌ์กฐ ๋ฐ ๋น„์ด์˜จํ™”์— ๋น„ํ•ด ์นจํˆฌ ํŠน์„ฑ์ด ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋‹ค๋ฅธ ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์— ๋น„ํ•ด ์žฅ์ ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์€ ์ œํ•œ๋œ ์˜์—ญ์—์„œ๋งŒ ์ƒ์—…์ ์œผ๋กœ ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ๊ณผ ํฌ๊ธฐ์— ๋”ฐ๋ผ ์‹œ์Šคํ…œ์ด ๋งค์šฐ ๊ณ ๊ฐ€์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋ฏธ์ง€๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ œ๊ณตํ•  ์ˆ˜ ์—†๋Š” ํ˜„ํ™ฉ์ด๋‹ค. ํ˜„์žฌ์˜ micro/mmW ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์˜ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๋ ค๋ฉด ์ƒˆ๋กœ์šด ์‹œ์Šคํ…œ ๊ฐœ๋…์„ ์ œ์•ˆํ•˜๊ณ  ํ…Œ์ŠคํŠธ๋ฒ ๋“œ๋ฅผ ์‹œ์—ฐํ•˜์—ฌ ์ž ์žฌ์ ์ธ ์ด์ ๊ณผ ์œ„ํ—˜์„ ์ž…์ฆํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Ku-band์™€ 1D-MIMO ์–ด๋ ˆ์ด๋ฅผ ์ด์šฉํ•œ ๊ด‘๋Œ€์—ญ micro/mmW ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์ธ Ku1DMIC๋ฅผ ์ œ์•ˆํ•˜์—ฌ ์œ„์™€ ๊ฐ™์€ ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ๋น„์šฉ ํšจ์œจ์ ์ธ 3์ฐจ์› ์˜์ƒํ™” ๊ธฐ๋Šฅ์„ ์œ„ํ•ด Ku1DMIC๋Š” 1D-MIMO ๋ฐฐ์—ด ๊ธฐ์ˆ ๊ณผ ISAR(Inverse Synthetic Aperture Radar) ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋™์‹œ์— Ku1DMIC๋Š” ์ฃผํŒŒ์ˆ˜ ๋ณ€์กฐ ์—ฐ์†ํŒŒ (FMCW) ๋ ˆ์ด๋”์™€์˜ ์›ํ™œํ•œ ์ธํ„ฐํŽ˜์ด์Šค์˜ ์‹œ์Šคํ…œ ์ˆ˜์ค€ ์„ค๊ณ„๋ฅผ ํ†ตํ•ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์„ ์ง€์›ํ•œ๋‹ค. Ku1DMIC๋ฅผ ์‚ฌ์šฉํ•œ 3์ฐจ์› ์˜์ƒํ™”์˜ ๊ตฌํ˜„ ๋ฐ ์‹ค์‹œ๊ฐ„ ๊ธฐ๋Šฅ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ๊ธฐ ์œ„ํ•ด, 2์ฐจ์› ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ 1D-MIMO RSA๊ณผ 3์ฐจ์› ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ 1D-MIMO-ISAR RSA๊ฐ€ ์ œ์•ˆ๋˜๊ณ  Ku1DMIC์—์„œ ๊ตฌํ˜„๋œ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๊ธฐ์—ฌ๋Š” Ku-band 1D-MIMO ๋ฐฐ์—ด ๊ธฐ๋ฐ˜ ์˜์ƒํ™” ์‹œ์Šคํ…œ ํ”„๋กœํ† ํƒ€์ž…์„ ๊ฐœ๋ฐœ ๋ฐ ํ…Œ์ŠคํŠธํ•˜๊ณ , ISAR ๊ธฐ๋ฐ˜ 3์ฐจ์› ์˜์ƒํ™” ๊ธฐ๋Šฅ์„ ๊ฒ€์‚ฌํ•˜๊ณ , ์‹ค์‹œ๊ฐ„ 3์ฐจ์› ์˜์ƒํ™” ๊ฐ€๋Šฅ์„ฑ์„ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋Œ€ํ•œ ์„ธ๋ถ€์ ์ธ ๊ธฐ์—ฌ ํ•ญ๋ชฉ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์‹ค์‹œ๊ฐ„ 2D ๊ทผ๊ฑฐ๋ฆฌ์žฅ ์ด๋ฏธ์ง• ๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ Ku ๋Œ€์—ญ MIMO ์ฃผํŒŒ์ˆ˜ ๋ณ€์กฐ ์—ฐ์†ํŒŒ(FMCW) ๋ ˆ์ด๋” ์‹คํ—˜ ํ”Œ๋žซํผ์ธ Ku1DMIC๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์‹œ์Šคํ…œ์€ ์ œํ•œ๋œ ์ˆ˜์˜ ์•ˆํ…Œ๋‚˜์—์„œ ๋” ๋„“์€ ์กฐ๋ช… ์˜์—ญ์„ ์ปค๋ฒ„ํ•˜๊ธฐ ์œ„ํ•ด Ku ๋Œ€์—ญ์„ ์‚ฌ์šฉํ•˜๊ณ  ๊ณ ํ•ด์ƒ๋„ ์ด๋ฏธ์ง€๋ฅผ ์ œ๊ณตํ•˜๋ฉด์„œ ๋น ๋ฅธ ๋ ˆ์ด๋” ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์„ ์œ„ํ•ด ๊ณ ์† ๋žจํ”„ ๋ฐ ๊ด‘๋Œ€์—ญ FMCW ํŒŒํ˜•์„ ์‚ฌ์šฉํ•œ๋‹ค. ํ”Œ๋žซํผ์˜ ํ•ต์‹ฌ ์„ค๊ณ„ ์›์น™์€ ์•ˆ์ •์„ฑ, ์žฌ๊ตฌ์„ฑ ๊ฐ€๋Šฅ์„ฑ ๋ฐ ์‹ค์‹œ๊ฐ„ ๊ธฐ๋Šฅ์œผ๋กœ ์‹œ์Šคํ…œ์˜ ๊ฐ•์ ๊ณผ ์•ฝ์ ์„ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํƒ์ƒ‰ํ•œ๋‹ค. ์„ค๊ณ„ ์›์น™์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•ด AMD-Xilinx UltraScale+ RFSoC(Radio Frequency System on Chip)๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋””์ง€ํ„ธ ์ง€์› ํ”Œ๋žซํผ์„ ์ œ์•ˆํ•˜๊ณ  ๊ตฌํ˜„ํ•œ๋‹ค. ์‹ค์‹œ๊ฐ„ 2D ์ด๋ฏธ์ง•์— ๋Œ€ํ•œ ์‹คํ—˜์  ์กฐ์‚ฌ๋Š” ์ดˆ๋‹น ์•ฝ 60ํ”„๋ ˆ์ž„์—์„œ ๋น„๋””์˜ค ์†๋„ ์ด๋ฏธ์ง•์˜ ๋Šฅ๋ ฅ์„ ์ž…์ฆํ–ˆ๋‹ค. ๋‘˜์งธ, ์˜์ƒ ํ’ˆ์งˆ ํ–ฅ์ƒ์„ ์œ„ํ•œ ํŒŒํ˜• ๋””์ง€ํ„ธ ์ „์น˜์™œ๊ณก(DPD) ๋ฐฉ๋ฒ•๊ณผ ๋ณด์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ตœ์ ํ™”๋œ ํŒŒํ˜• ๋ฐœ์ƒ๊ธฐ์˜ ๋„์›€์œผ๋กœ ๊นจ๋—ํ•œ FMCW ํŒŒํ˜•์ด ์ƒ์„ฑ๋˜๋”๋ผ๋„ ํŠนํžˆ ํ”Œ๋žซํผ์˜ RF ํ•˜์œ„ ์‹œ์Šคํ…œ์—์„œ ์‹ ํ˜ธ๋Š” ํ•„์—ฐ์ ์œผ๋กœ ์™œ๊ณก์„ ๊ฒช๊ฒŒ๋œ๋‹ค. ๊ทผ๊ฑฐ๋ฆฌ ์˜์ƒํ™” ์‘์šฉ ๋ถ„์•ผ์—์„œ๋Š” ํŒŒํ˜• DPD๋Š” ๊ด‘๋Œ€์—ญ FMCW ๋ ˆ์ด๋” ์‹œ์Šคํ…œ์˜ ์™œ๊ณก์„ ์–ต์ œํ•˜๋Š” ๋ฐ ํšจ๊ณผ์ ์ด์ง€ ์•Š๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Ku1DMIC์—์„œ ํŒŒํ˜• DPD๊ฐ€ ์œ ํšจํ•˜๋„๋ก LO-DPD ์•„ํ‚คํ…์ฒ˜์™€ ์ด์ง„ ํƒ์ƒ‰ ๊ธฐ๋ฐ˜ DPD ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ํŒŒํ˜• DPD๋กœ ์ œ๊ฑฐํ•  ์ˆ˜ ์—†๋Š” ๋‚˜๋จธ์ง€ ์˜ค๋ฅ˜๋ฅผ ๋ณด์ƒํ•˜๊ธฐ ์œ„ํ•ด ์ด๋ฏธ์ง€ ์˜์—ญ ์ตœ์ ํ™” ๋ณด์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ๋ฐ ํด๋Ÿฌํ„ฐ ์‹ ํ˜ธ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ์›์น˜ ์•Š๋Š” ์‹ ํ˜ธ์— ๋Œ€ํ•œ ๊ฒฌ๊ณ ์„ฑ์„ ์œ„ํ•ด ์ผ๋ฐ˜ํ™”๋œ Tikhonov ์ •๊ทœํ™” ๋ฐ ์ „์ฒด ๋ณ€๋™(TV) ์ •๊ทœํ™”๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ์ •๊ทœํ™”๋œ ์ตœ์†Œ ์ž์Šน ๋ฌธ์ œ๋ฅผ ์ ์šฉ ํ›„ ๋น„๊ตํ•œ๋‹ค. ๋‹ค์–‘ํ•œ 2์ฐจ์› ์˜์ƒํ™” ์‹คํ—˜์„ ํ†ตํ•ด ๋‘ ๋ฐฉ๋ฒ• ๋ชจ๋‘ ๋ถ€์—ฝ ๋ ˆ๋ฒจ์„ ์ค„์—ฌ ํ™”์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ISAR ๊ธฐ๋ฒ•์„ 2์ฐจ์› ์˜์ƒ ํ”Œ๋žซํผ์— ์ ์šฉํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ 3์ฐจ์› ์˜์ƒ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•œ๋‹ค. 1D-MIMO-ISAR ๊ตฌ์„ฑ์—์„œ ์‹ค์‹œ๊ฐ„ 3D ์ด๋ฏธ์ง•์˜ ๊ตฌํ˜„์€ ์ด๋Ÿฌํ•œ ๊ตฌ์„ฑ์ด 3D ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์˜ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜ํ–ฅ๋ ฅ์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์—์„œ๋Š” 1D-MIMO-ISAR ๊ตฌ์„ฑ์— ๋Œ€ํ•œ ์ด๋ฏธ์ง• ์žฌ๊ตฌ์„ฑ์„ ๊ฐ€์†ํ™”ํ•˜๊ธฐ ์œ„ํ•ด 1D-MIMO-ISAR ๋ฒ”์œ„ ์Šคํƒœํ‚น ์•Œ๊ณ ๋ฆฌ์ฆ˜(RSA)์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ 1D-MIMO-ISAR RSA๋Š” ๋„๋ฆฌ ์•Œ๋ ค์ง„ Back-Projection ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ฑฐ์˜ ๋™์ผํ•œ ์ด๋ฏธ์ง€ ํ’ˆ์งˆ์„ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ์ˆ˜๋ฐฑ ๋ฐ€๋ฆฌ์ดˆ ์ด๋‚ด์— ์ด๋ฏธ์ง€๋ฅผ ์žฌ๊ตฌ์„ฑํ•จ์œผ๋กœ์จ ์‹ค์‹œ๊ฐ„ ์˜์ƒํ™”์— ๋Œ€ํ•œ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ ๋ฌผ์ฒด๊ฐ€ ์‹œ์•ผ์— ๋“ค์–ด์˜ค๊ณ  ๋‚˜๊ฐ€๋Š” ์‹ค์ œ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•œ ROI ์„ค์ •, ๊ทธ๋ฆฌ๊ณ  ๋ฉ”๋ชจ๋ฆฌ ํ• ๋‹น์— ๋Œ€ํ•œ ์ „๋žต์„ ์„ค๋ช…ํ•œ๋‹ค. ๊ด‘๋ฒ”์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์„ ํ†ตํ•ด 1D-MIMO-IASR ๊ตฌ์„ฑ ๋ฐ 1D-MIMO-ISAR RSA์˜ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์ž ์žฌ์  ์ด์ ์„ ํ™•์ธํ•œ๋‹ค.1 INTRODUCTION 1 1.1 Microwave and millimeter-wave imaging 1 1.2 Imaging with radar system 2 1.3 Challenges and motivation 5 1.4 Outline of the dissertation 8 2 FUNDAMENTAL OF TWO-DIMENSIONAL IMAGING USING A MIMO RADAR 9 2.1 Signal model 9 2.2 Consideration of waveform 12 2.3 Image reconstruction algorithm 16 2.3.1 Back-projection algorithm 16 2.3.2 1D-MIMO range-migration algorithm 20 2.3.3 1D-MIMO range stacking algorithm 27 2.4 Sampling criteria and resolution 31 2.5 Simulation results 36 3 MIMO-FMCW RADAR IMPLEMENTATION WITH 16 TX - 16 RX ONE- DIMENSIONAL ARRAYS 46 3.1 Wide-band FMCW waveform generator architecture 46 3.2 Overall system architecture 48 3.3 Antenna and RF transceiver module 53 3.4 Wide-band FMCW waveform generator 55 3.5 FPGA-based digital hardware design 63 3.6 System integration and software design 71 3.7 Testing and measurement 75 3.7.1 Chirp waveform measurement 75 3.7.2 Range profile measurement 77 3.7.3 2-D imaging test 79 4 METHODS OF IMAGE QUALITY ENHANCEMENT 84 4.1 Signal model 84 4.2 Digital pre-distortion of chirp signal 86 4.2.1 Proposed DPD hardware system 86 4.2.2 Proposed DPD algorithm 88 4.2.3 Measurement results 90 4.3 Robust calibration method for signal distortion 97 4.3.1 Signal model 98 4.3.2 Problem formulation 99 4.3.3 Measurement results 105 5 THREE-DIMENSIONAL IMAGING USING 1-D ARRAY SYSTEM AND ISAR TECHNIQUE 110 5.1 Formulation for 1D-MIMO-ISAR RSA 111 5.2 Algorithm implementation 114 5.3 Simulation results 120 5.4 Experimental results 122 6 CONCLUSIONS AND FUTURE WORK 127 6.1 Conclusions 127 6.2 Future work 129 6.2.1 Effects of antenna polarization in the Ku-band 129 6.2.2 Forward-looking near-field ISAR configuration 130 6.2.3 Estimation of the movement errors in ISAR configuration 131 Abstract (In Korean) 145 Acknowlegement 148๋ฐ•

    Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

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    Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

    An FMCW radar system for short range surface contour mapping

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    The surface of a fluidized bed combustor may be indicative of problems, such as agglomeration, occurring within the bed material. The imaging system described in this dissertation has the potential of becoming a diagnostic tool to serve this purpose. The goal of this project was to design and evaluate a radar-based, phased-array system for capturing the surface contours of static surfaces located short distances (approximately 8 ft.) from the antenna array;The energy source for the contour mapping system was a frequency modulated, continuous wave (FMCW) radar. Since the frequency of a demodulated FCMW signal is proportional to the distance to the target, the dominant frequency component of the output from the beamforming algorithm is an estimate of the distance to the surface;This dissertation describes the equipment arrangement, the noise removal methods, and the delay and sum beamforming algorithm. In addition, results for several different surface types and structures are presented and discussed. Since it is shown that this prototype system has limitations, recommendations for future modifications are also suggested

    A Study of Types of Sensors used in Remote Sensing

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    Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

    Through-The-Wall Detection Using Ultra Wide Band Frequency Modulated Interrupted Continuous Wave Signals

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    Through-The-Wall-Detection (TTWD) techniques can improve the situational awareness of police and soldiers, and support first responders in search and rescue operations. A variety of systems for TTWD based on different waveforms have been developed and presented in the literature, e.g. radar systems based on pulses, noise or pseudo-noise waveforms, and frequency modulated continuous wave (FMCW) or stepped frequency continuous wave (SFCW) waveforms. Ultra wide band signals are normally used as they provide suitable resolution to discriminate different targets. A common problem for active radar systems for TTWD is the strong backscattered signal from the air-wall interface. This undesired signal can overshadow the reflections from actual targets, especially those with low radar cross section like human beings, and limit the dynamic range at the receiver, which could be saturated and blocked. Although several techniques have been developed to address this problem, frequency modulated interrupted continuous wave (FMICW) waveforms represent an interesting further approach to wall removal, which can be used as an alternative technique or combined with the existing ones. FMICW waveforms have been used in the past for ionospheric and ocean sensing radar systems, but their application to the wall removal problem in TTWD scenarios is novel. The validation of the effectiveness of the proposed FMICW waveforms as wall removal technique is therefore the primary objective of this thesis, focusing on comparing simulated and experimental results using normal FMCW waveforms and using the proposed FMICW waveforms. Initially, numerical simulations of realistic scenarios for TTWD have been run and FMICW waveforms have been successfully tested for different materials and internal structure of the wall separating the radar system and the targets. Then a radar system capable of generating FMICW waveforms has been designed and built to perform a measurement campaign in environments of the School of Engineering and Computing Sciences, Durham University. These tests aimed at the localization of stationary targets and at the detection of people behind walls. FMICW waveforms prove to be effective in removing/mitigating the undesired return caused by antenna cross-talk and wall reflections, thus enhancing the detection of targets

    Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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    Topography measurements using an airborne Ka-band FMCW Interferometric Synthetic Aperture Radar

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    Radar interferometry at millimeter-wave frequencies has the ability of topography measurement of different types of terrain, such as water surfaces and tree canopies. A Ka-band interferometric radar was mounted on an airborne platform, and flown over the Connecticut river region in western Massachusetts near Amherst on June 11, 2012. More than 20 Gigabytes of raw data was recorded. This dissertation outline presents the results of the data processing, which includes (1) the estimation and removal of the embedded high frequency phase error in the raw data; (2) the synthetic aperture processing; (3) the interferometric processing. The digital elevation model (DEM) has been generated based on an external DEM from MassGIS. The accuracy of the topography measurement can be affected by many factors, such as the de-correlation between channels and the residual phase error. This dissertation outline proposes to evaluate the accuracy of the topography measurement by quantifying each error source. Specifically, the performance of the phase error estimator and the Doppler centroid estimation will be analyzed, and an algorithm which estimates the attitudes of the airborne platform will be developed

    Time-to-digital converters and histogram builders in SPAD arrays for pulsed-LiDAR

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    Light Detection and Ranging (LiDAR) is a 3D imaging technique widely used in many applications such as augmented reality, automotive, machine vision, spacecraft navigation and landing. Pulsed-LiDAR is one of the most diffused LiDAR techniques which relies on the measurement of the round-trip travel time of an optical pulse back-scattered from a distant target. Besides the light source and the detector, Time-to-Digital Converters (TDCs) are fundamental components in pulsed-LiDAR systems, since they allow to measure the back-scattered photon arrival times and their performance directly impact on LiDAR system requirements (i.e., range, precision, and measurements rate). In this work, we present a review of recent TDC architectures suitable to be integrated in SPAD-based CMOS arrays and a review of data processing solutions to derive the TOF information. Furthermore, main TDC parameters and processing techniques are described and analyzed considering pulsed-LiDAR requirements
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