2,599 research outputs found

    An Efficient ISAR Imaging Method for Multiple Targets

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    This paper proposes an efficient method to obtain TSAR images of multiple targets flying in formation. The proposed method improves the coarse alignment and segmentation of the existing method. The improved coarse alignment method models the flight trajectory using a combination of a polynomial and Gaussian basis functions, and the optimum parameter of the trajectory is found using particle swarm optimization. In the improved segmentation, the binary image of the bulk TSAR image that contains all targets is constructed using a two-dimensional constant false alarm detector, then the image closing method is applied to the binary image. Finally, the connected set of binary pixels is used to segment each target from the bulk image. Simulations using three targets composed of point scattering centers and the measured data of the Boeing747 aircraft prove the effectiveness of the proposed method to segment three targets flying in formation.X113Ysciescopu

    Anti-jamming of Inverse Synthetic Aperture Radar based on Slope-varying Linear Frequency Modulation Signal

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    Deceptive jamming technology against inverse synthetic aperture radar is matured now, which is meaningful in military application. But the research on anti-jamming technology for inverse synthetic aperture radar (ISAR) is still not a mature technology. Through the analysis on the theory of deceptive jamming technology against ISAR, a new method for anti-jamming against ISAR based on linear frequency modulation signals frequency slope-varying is presented. The false target echo energy is suppressed due to frequency modulation slope mis-matching. Doppler domain averaging is adopted for improving the quality of the ISAR image, which helps automatic target recognition. Simulation result based on simulating data shows the validity of the new algorithm.Defence Science Journal, 2009,ย 59(5), pp.537-544,ย DOI:http://dx.doi.org/10.14429/dsj.59.155

    Digital Image Processing

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    This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

    Application of Discrete Wavelet Transform in Watermarking

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    Current-mode processing based Temperature-to-Digital Converters for MEMS applications

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    This thesis presents novel Temperature-to-Digital Converters (TDCs) designed and fabricated in CMOS technology. These integrated smart temperature sensing circuits are widely employed in the Micro-Electro-Mechanical Systems (MEMS) field in order to mitigate the impact of the ambient temperature on their performance. In this framework, the increasingly stringent demands of the market have led the cost-effectiveness specification of these compensation solutions to an higher and higher level, directly translating into the requirement of more and more compact designs (< 0.1 mmยฒ); in addition to this, considering that the great majority of the systems whose thermal drift needs to be compensated is battery supplied, ultra-low energy-per-conversion (< 10 nJ) is another requirement of primary importance. This thesis provides a detailed description of two different test-chips (mas fuerte and es posible) that have been designed with this orientation and that are the result of three years of research activity; for both devices, the conception, design, layout and testing phases are all described in detail and are supported by simulation and measurement results.This thesis presents novel Temperature-to-Digital Converters (TDCs) designed and fabricated in CMOS technology. These integrated smart temperature sensing circuits are widely employed in the Micro-Electro-Mechanical Systems (MEMS) field in order to mitigate the impact of the ambient temperature on their performance. In this framework, the increasingly stringent demands of the market have led the cost-effectiveness specification of these compensation solutions to an higher and higher level, directly translating into the requirement of more and more compact designs (< 0.1 mmยฒ); in addition to this, considering that the great majority of the systems whose thermal drift needs to be compensated is battery supplied, ultra-low energy-per-conversion (< 10 nJ) is another requirement of primary importance. This thesis provides a detailed description of two different test-chips (mas fuerte and es posible) that have been designed with this orientation and that are the result of three years of research activity; for both devices, the conception, design, layout and testing phases are all described in detail and are supported by simulation and measurement 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•

    DVB-T Receiver Independent of Channel Allocation, with Frequency Offset Compensation for Improving Resolution in Low Cost Passive Radar.

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    In this article, a commercial low cost solution for increasing DVB-T based passive radar (also referred to as passive coherentlocation)robustnesswith respectto channel allocation and range resolution, is designed. IDEPAR demonstrator is updated to include a new recording system in charge of DVB-T channels reallocation. The use of commercial hardware introduces frequency mismatching between acquired channels that compromises system operation. To face channel frequency alignment, fulfilling the requirements imposed by the high Doppler resolution typical of passive radars, a novel compensation algorithm is proposed, based on the minimization of signal dispersion in the Cross-Ambiguity Function domain. The well-known cyclic prefix van de Beek method is used as reference. Results show an increase in target signal to interference ratio and detection performances, as well as a reduction of clutter dispersion when the novel proposed algorithm is applied. The low cost solution is also compared to a high performance one capable of acquiring a wide bandwidth of sparse DVB-T channels, and performing channel reallocation by digital signal processing. Results show that both systems reached similar performances in terms of range resolution and SNR improvement
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