122 research outputs found

    140GHzๅธฏใƒŸใƒชๆณขใƒฌใƒผใƒ€ใฎใŸใ‚ใฎRPMๆณ•ใซใ‚ˆใ‚‹้ซ˜้€Ÿใƒป้ซ˜็ฒพๅบฆ็ซ‹ไฝ“็”ปๅƒๅŒ–

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    ่ถ…ๅบƒๅธฏๅŸŸใƒฌใƒผใƒ€ใฏ้ซ˜ใ„่ท้›ขๅˆ†่งฃ่ƒฝใ‚’ๆœ‰ใ—๏ผŒๅ…‰ๅญฆใ‚ปใƒณใ‚ตใฎ้‹็”จใŒๅ›ฐ้›ฃใช็Šถๆณใง่จˆๆธฌใŒๅฏ่ƒฝใงใ‚ใ‚Š๏ผŒๆœ‰็”จใจใ•ใ‚Œใฆใ„ใ‚‹๏ผŽ่ฟ‘ๅนด๏ผŒ140GHzๅธฏใฎ่ถ…ๅบƒๅธฏๅŸŸใƒฌใƒผใƒ€ใŒ๏ผŒ่ฃ…็ฝฎใฎๅฐๅž‹ๅŒ–ใ‚„้ซ˜ๅˆ†่งฃ่ƒฝๅŒ–ใฎ่ฆณ็‚นใ‹ใ‚‰ๆณจ็›ฎใ•ใ‚ŒใฆใŠใ‚Š๏ผŽๅปบ่จญ็พๅ ดใซใŠใ‘ใ‚‹้‡ๆฉŸใฎ็›ฎๆจ™็‰ฉ่ช่ญ˜ใ‚ปใƒณใ‚ตใ‚„ๆ•‘ๅŠฉใƒญใƒœใƒƒใƒˆ๏ผŒ้ซ˜้ฝข่€…่ฆ‹ๅฎˆใ‚Šใ‚ทใ‚นใƒ†ใƒ ็ญ‰ใซๆœ‰็”จใจใ•ใ‚Œใฆใ„ใ‚‹๏ผŽ็›ฎๆจ™ๅฝข็Šถใฎ็”ปๅƒๅŒ–ๆŠ€่ก“ใจใ—ใฆ๏ผŒๆ—ขใซๅˆๆˆ้–‹ๅฃๅ‡ฆ็†(SAR: Synthetic Aperture Radar)ใ‚„๏ผŒSEABED(Shape Estimation Algorithm based on Boundary scattering transform and Extraction of Directly scattered waves)ๆณ•ๅŠใณRPM(Range Points Migration)ๆณ•็ญ‰ใŒๆๆกˆใ•ใ‚Œใฆใ„ใ‚‹๏ผŒใใฎไธญใง๏ผŒRPMๆณ•ใฏ่ฆณๆธฌใ•ใ‚Œใ‚‹่ท้›ขใจ็ด ๅญใ‚’็ดใฅใ‘ใ—ใŸ่ท้›ข็‚นใ‚’็›ฎๆจ™ๅขƒ็•Œใฎๆ•ฃไนฑไธญๅฟƒ็‚นใซไธ€ๅฏพไธ€ใงๅ†™ๅƒใ•ใ›ใ‚‹ๆ–นๆณ•ใงใ‚ใ‚Š๏ผŒ็›ฎๆจ™ใŒ่ค‡ๆ•ฐใง่ค‡้›‘ใชๅฝข็Šถใ‚’ใ‚‚ใคๅ ดๅˆใซใงใ‚‚ใƒšใ‚ขใƒชใƒณใ‚ฐ็ญ‰ใฎๅ‡ฆ็†ใ‚’็ตŒใšใซ้ซ˜้€Ÿใ‹ใค้ซ˜็ฒพๅบฆใชๅขƒ็•ŒๆŽจๅฎšใŒๅฎŸ็พใ•ใ‚Œใ‚‹ใ“ใจใŒๅฎŸ่จผใ•ใ‚Œใฆใ„ใ‚‹๏ผŽใ—ใ‹ใ—ใชใŒใ‚‰๏ผŒๅŒๆ‰‹ๆณ•ใฏๅ†™ๅƒๅ‡ฆ็†ใซใŠใ„ใฆ๏ผŒๅ…จใฆใฎ่ท้›ข็‚นใ‚’ไธ€ๆ‹ฌๅ‡ฆ็†ใ™ใ‚‹ใŸใ‚๏ผŒๅคš็›ฎๆจ™็’ฐๅขƒไธ‹ใซใŠใ„ใฆ่จˆ็ฎ—้‡ใŒ่†จๅคงใซใชใ‚‹ๅ•้กŒใŒใ‚ใ‚‹๏ผŽใพใŸ่ท้›ข็‚นใ‚’ๅ†™ๅƒใ™ใ‚‹้š›ใซ๏ผŒไป–ใฎ็›ฎๆจ™ใซๅฑžใ™ใ‚‹ใฎ่ท้›ข็‚นใŒๅซใพใ‚Œใฆใ„ใ‚‹ๅ ดๅˆ๏ผŒ็”ปๅƒๅŒ–็ฒพๅบฆใŒๅŠฃๅŒ–ใ™ใ‚‹ๅ•้กŒใŒใ‚ใ‚‹๏ผŽๆœฌ่ซ–ๆ–‡ใงใฏ๏ผŒๅŒๅ•้กŒใ‚’่งฃๆฑบใ™ใ‚‹ใŸใ‚๏ผŒ่ท้›ข็‚นใ‚’็›ฎๆจ™ๆฏŽใซไบ‹ๅ‰ใซใ‚ฏใƒฉใ‚นใ‚ฟใƒชใƒณใ‚ฐใ™ใ‚‹ใ“ใจใงๅ‡ฆ็†้€Ÿๅบฆใ‚’้ซ˜้€ŸๅŒ–ใ™ใ‚‹ใจใจใ‚‚ใซ็ฒพๅบฆใ‚’ๅ‘ไธŠใ•ใ›ใ‚‹ๆ–นๆณ•ใ‚’ๆๆกˆใ™ใ‚‹๏ผŽไธ€ๆ–น๏ผŒ็›ฎๆจ™ใ‚’ไบ‹ๅ‰ใซใ‚ฏใƒฉใ‚นใ‚ฟใƒชใƒณใ‚ฐใ™ใ‚‹ใŸใ‚ใซใฏ๏ผŒ่ท้›ขใฎๆƒ…ๅ ฑใฎใฟใงใฏๆฅตใ‚ใฆๅ›ฐ้›ฃใงใ‚ใ‚‹ใ“ใจใŒ็ขบ่ชใ•ใ‚Œใฆใ„ใ‚‹๏ผŽๆœฌ่ซ–ๆ–‡ใงใฏ็›ฎๆจ™ใซ้–ขใ™ใ‚‹ๅ…ˆ้จ“็š„ใชๆƒ…ๅ ฑใ‚’็”จใ„ใšใซๅŒใ‚ฏใƒฉใ‚นใ‚ฟใƒชใƒณใ‚ฐใ‚’ๅฎŸ็พใ™ใ‚‹ๆ–นๆณ•ใ‚’่ค‡ๆ•ฐๆๆกˆใ™ใ‚‹๏ผŽ็ฌฌไธ€ใซ๏ผŒใ‚ฏใƒฉใ‚นใ‚ฟใƒชใƒณใ‚ฐใ‚’RPMๆณ•ใงๅ†™ๅƒใ•ใ‚ŒใŸๅฎŸ็ฉบ้–“ใงๅฎŸๆ–ฝใ™ใ‚‹ๆ–นๆณ•ใ‚’็คบใ™๏ผŽๅ‡ฆ็†้€Ÿๅบฆใ‚’็Š ็‰ฒใซใ—ใชใ„ใŸใ‚๏ผŒ่ฉ•ไพกใ™ใ‚‹็ด ๅญใฎ็ต„ๅˆใ›ใ‚’็ตžใ‚Š่พผใ‚€๏ผŽ็ฌฌไบŒใซ๏ผŒ็›ฎๆจ™ใฎๅ„้ƒจไฝใŒ็•ฐใชใ‚‹้€Ÿๅบฆใ‚’ๆœ‰ใ—ใฆใ„ใ‚‹ใ“ใจใ‚’ๅ‰ๆใจใ—ใฆ๏ผŒใƒ‰ใƒƒใƒ—ใƒฉ้€Ÿๅบฆใซใ‚ˆใ‚Š่ท้›ข็‚นใ‚’ใ‚ฏใƒฉใ‚นใ‚ฟใƒชใƒณใ‚ฐใ™ใ‚‹ๆ–นๆณ•ใ‚’็คบใ™๏ผŽ140GHzๅธฏใƒฌใƒผใƒ€ใ‚’ๆƒณๅฎšใ—ใŸๆ•ฐๅ€ค่จˆ็ฎ—ใซใ‚ˆใ‚‹ๆ€ง่ƒฝ่ฉ•ไพกใซใ‚ˆใ‚Š๏ผŒๆๆกˆใ™ใ‚‹ไธกๆ‰‹ๆณ•ใฏๅพ“ๆฅใซๆฏ”ใ—ใฆ๏ผŒๅ‡ฆ็†้€ŸๅบฆๅŠใณ็ฒพๅบฆใ‚„ๅ†็พ้ ˜ๅŸŸ็ญ‰ใ‚’้ฃ›่บ็š„ใซๆ”นๅ–„ใ™ใ‚‹ใ“ใจใ‚’็คบใ™๏ผŽๆœ€ๅพŒใซๆœฌๆ‰‹ๆณ•ใฎๅฎŸๆ™‚้–“ๅ‡ฆ็†ใธใฎๅฏ่ƒฝๆ€งใ‚’ๆคœ่จŽใ™ใ‚‹ใŸใ‚๏ผŒใƒขใƒŽใ‚นใ‚ฟใƒ†ใ‚ฃใƒƒใ‚ฏ่ฟ‘ไผผใ‚’็”จใ„ใŸRPMๆณ•ใฎ้ซ˜้€ŸๅŒ–ๆณ•ใ‚’ๆ็คบใ—๏ผŒใใฎๆœ‰ๅŠนๆ€งใ‚’็คบใ™๏ผŽ้›ปๆฐ—้€šไฟกๅคงๅญฆ201

    Radar Technology

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    In this book โ€œRadar Technologyโ€, the chapters are divided into four main topic areas: Topic area 1: โ€œRadar Systemsโ€ consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: โ€œRadar Applicationsโ€ shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: โ€œRadar Functional Chain and Signal Processingโ€ describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: โ€œRadar Subsystems and Componentsโ€ consists of design technology of radar subsystem components like antenna design or waveform design

    Wave tomography

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    Short-Range Super-Resolution Feature Extraction of Complex Edged Contours for Object Recognition by Ultra-Wideband Radar

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    This thesis contributes to the field of short-range ultra-wideband (UWB) Radar. In particular, an object recognition approach performed by a bi-static UWB Radar has been investigated in this thesis. The investigated objects consist of simple canonical and some polygonal complex objects which are scanned on a circular track at about 1 m distance. Geometrical features, texture features and moment based features are extracted from the Radar data to carry out the recognition. Yet, the precise temporal evolution is subject to massive distortions, mainly caused by severe interference conditions and transient effects of the hardware. Thus, super-resolution algorithms have been developed which go far beyond the classical bandwidth given resolution and asked for research on various fields: (i) An innovative wavefront extraction algorithm with polarimetric diversity exploitation has been developed to separate pulses which overlap almost the whole pulse duration; (ii) a highly precise feature extraction algorithm has been developed which localises significant scattering centres by processing the previously extracted wavefronts; (iii) a novel UWB object recognition algorithm has been developed to classify and discriminate the resulting microwave images. When scanning objects from all sides, exceptional recognition of objects was achieved by a minimum mean squared error classifier. Further improvement in recognition was obtained, especially at severly restricted tracks, by the application of Bayes theory which constitutes a superior classifier to the above. In addition to the main field of research, a novel stereoscopic 3D UWB imaging algorithm, based on a spatially spanned synthetic aperture in conjunction with ellipsoidal shaped wavefronts, has been developed. The ultimate test of any model and system is an experimental validation. Consequently in this thesis, all developed algorithms and the object recognition as a whole system are experimentally validated within an elaborate measurement campaign

    Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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    Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs

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    77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadaysโ€™ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation. In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods. In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods. In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd

    FMCW Signals for Radar Imaging and Channel Sounding

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    A linear / stepped frequency modulated continuous wave (FMCW) signal has for a long time been used in radar and channel sounding. A novel FMCW waveform known as โ€œGated FMCWโ€ signal is proposed in this thesis for the suppression of strong undesired signals in microwave radar applications, such as: through-the-wall, ground penetrating, and medical imaging radar. In these applications the crosstalk signal between antennas and the reflections form the early interface (wall, ground surface, or skin respectively) are much stronger in magnitude compared to the backscattered signal from the target. Consequently, if not suppressed they overshadow the targetโ€™s return making detection a difficult task. Moreover, these strong unwanted reflections limit the radarโ€™s dynamic range and might saturate or block the receiver causing the reflection from actual targets (especially targets with low radar cross section) to appear as noise. The effectiveness of the proposed waveform as a suppression technique was investigated in various radar scenarios, through numerical simulations and experiments. Comparisons of the radar images obtained for the radar system operating with the standard linear FMCW signal and with the proposed Gated FMCW waveform are also made. In addition to the radar work the application of FMCW signals to radio propagation measurements and channel characterisation in the 60 GHz and 2-6 GHz frequency bands in indoor and outdoor environments is described. The data are used to predict the bit error rate performance of the in-house built measurement based channel simulator and the results are compared with the theoretical multipath channel simulator available in Matlab

    Noncontact Vital Signs Detection

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    Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

    ์‹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•
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