Relevant bibliographies by topics / Micro-Doppler radar

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Micro-Doppler radar'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Micro-Doppler radar"

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Gong, Jiangkun, Jun Yan, Deren Li, and Deyong Kong. "Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times." Drones 6, no. 9 (September 19, 2022): 262. http://dx.doi.org/10.3390/drones6090262.

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Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.
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Krasnov, Oleg A., and Alexander G. Yarovoy. "Radar micro-Doppler of wind turbines: simulation and analysis using rotating linear wire structures." International Journal of Microwave and Wireless Technologies 7, no. 3-4 (June 2015): 459–67. http://dx.doi.org/10.1017/s1759078715000641.

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A simple electromagnetic model of wind-turbine's main structural elements as the linear wired structures is developed to simulate the temporal patterns of observed radar return Doppler spectra (micro-Doppler). Using the model, the micro-Doppler for different combinations of the turbines rotation frequency, radar pulse repetition frequency, and duration of the Doppler measurement interval are analyzed. The model is validated using the PARSAX radar experimental data. The model ability to reproduce the observed Doppler spectra main features can be used for development of signal-processing algorithms to suppress the wind-turbines clutter in modern Doppler radars.
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Hassan, Shahid, Xiangrong Wang, Saima Ishtiaq, Nasim Ullah, Alsharef Mohammad, and Abdulfattah Noorwali. "Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar." Remote Sensing 15, no. 7 (March 24, 2023): 1752. http://dx.doi.org/10.3390/rs15071752.

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Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved.
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Wang, Zhihao, Ying Luo, Kaiming Li, Hang Yuan, and Qun Zhang. "Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna." Remote Sensing 14, no. 11 (May 26, 2022): 2549. http://dx.doi.org/10.3390/rs14112549.

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Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.
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Zang, Bo, Mingzhe Zhu, Xianda Zhou, Lu Zhong, and Zijiao Tian. "Application of S-Transform Random Consistency in Inverse Synthetic Aperture Imaging Laser Radar Imaging." Applied Sciences 9, no. 11 (June 5, 2019): 2313. http://dx.doi.org/10.3390/app9112313.

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Under the same principle, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length, as the characteristic of multi-resolution, S transform could reduce the influence of the micro-Doppler component and enhance the imaging effect. This paper presents a method for micro-Doppler feature extraction in Inverse Synthetic Aperture Imaging Laser Radar (ISAIL) imaging. It is accessible and comprehensive, applying Random Sample Consensus (RANSAC) for the separation and reconstruction of micro-Doppler and rigid body signals. Experiments show that the method can effectively remove the micro-Doppler information and obtain a clear target distance-instantaneous Doppler image.
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Singh, Ashish Kumar, and Yong-Hoon Kim. "Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN." Traitement du Signal 38, no. 4 (August 31, 2021): 1033–39. http://dx.doi.org/10.18280/ts.380413.

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The development of advanced radar system for detection and classification of UAVs is an essential requirement for today’s societal security. Such intelligent system could able to analyze the received radar signal and extract relevant information by utilizing sophisticated algorithm. In this letter, the utilization of micro-Doppler signature (MDS) for classification of drones, using convolutional neural network (CNN) model has been presented. We have generated images of micro-Doppler signatures using W-band radar system and used it for classification purpose. In this work, phase stretch transform (PST) has been utilized for edge detection and enhancement of the micro-Doppler images, to generate the edge-enhanced micro-Doppler image (EMDI). The comparison based on classification performance of CNN with different input datasets shows that the EMDI based CNN model outperformed the micro-Doppler image (MDI) based model.
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Harsh, Archit. "Measuring Radar Signatures of a Simple Pendulum using Cantenna Radar." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 5 (April 13, 2016): 6785–95. http://dx.doi.org/10.24297/ijct.v15i5.1653.

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This paper gives a detailed analysis of the physics of simple pendulum and the equations governing the motion and velocity. The pendulum works in three modes: simple, damped and driving and driving only. The signatures are evaluated and simulated by the means of four different approaches: Euler method, Euler-Cromer method, 2nd order Runge-kutta method and built-in ODE-23 matlab solver. The simulation results are compared to the measured radar signatures using a CANTENNA RADAR originally developed by MIT. The radar was operated in Doppler mode and the micro-Doppler effects associated with pendulum is studied. This paper attempts to provide an in-depth background and analysis of how the pendulum works and the associated micro-Doppler study using RADAR.
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Ding, Yipeng, Chengxi Lei, Xuemei Xu, Kehui Sun, and Ling Wang. "Human Micro-Doppler Frequency Estimation Approach for Doppler Radar." IEEE Access 6 (2018): 6149–59. http://dx.doi.org/10.1109/access.2018.2793277.

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Molchanov, Pavlo, Ronny I. A. Harmanny, Jaco J. M. de Wit, Karen Egiazarian, and Jaakko Astola. "Classification of small UAVs and birds by micro-Doppler signatures." International Journal of Microwave and Wireless Technologies 6, no. 3-4 (March 19, 2014): 435–44. http://dx.doi.org/10.1017/s1759078714000282.

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The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.
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Cui, Rui, Ai Guo Sheng, Ji Fei Pan, Bing He, and Jing Zhu. "Research on Jamming Method of False Target Based on Micro-Motion Modulation." Applied Mechanics and Materials 556-562 (May 2014): 2707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2707.

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Micro-Doppler is a unique feature of radar target, and has been applied to target recognition of ISAR widely, but it can also destroy the quality of the target image at the same time. So a novel jamming method of false target base on Micro-Doppler modulation is presented in the paper. The phase of captured radar transmitting signal is been modulated, which can generate false Micro-Doppler features. The micro-Doppler imaging model of the rotating target is analyzed, and the jamming model based on Micro-Motion modulation is given. Finally, the simulation of jamming experiment is carried out. The results of simulation prove the method is corrective and effective.
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Dissertations / Theses on the topic "Micro-Doppler radar"

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Smith, G. E. "Radar target micro-Doppler signature classification." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/18688/.

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This thesis reports on research into the field of Micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR) with additional contributions to general radar ATR methodology. The μ-DS based part of the research contributes to three distinct areas: time domain classification; frequency domain classification; and multiperspective μ-DS classification that includes the development of a theory for the multistatic μ-DS. The contribution to general radar ATR is the proposal of a methodology to allow better evaluation of potential approaches and to allow comparison between different studies. The proposed methodology is based around a “black box” model of a radar ATR system that, critically, includes a threshold to detect inputs that are previously unknown to the system. From this model a set of five evaluation metrics are defined. The metrics increase the understanding of the classifier’s performance from the common probability of correct classification, that reports how often the classifier correctly identifies an input, to understanding how reliable it is, how capable it is of generalizing from the reference data, and how effective its unknown input detection is. Additionally, the significance of performance prediction is discussed and a preliminary method to estimate how well a classifier should perform is developed. The proposed methodology is then used to evaluate the μ-DS based radar ATR approaches considered. The time domain classification investigation is based around using Dynamic Time Warping (DTW) to identify radar targets based on their μ-DS. DTW is a speech processing technique that classifies data series by comparing them with a pre-classified reference dataset. This is comparable to the common k-Nearest Neighbour (k-NN) algorithm, so k-NN is used as a benchmark against which to evaluate DTW’s performance. The DTW approach is observed to work well. It achieved high probability of correct classification and reliability as well as being able to detect inputs of unknown class. However, the classifier’s ability to generalize from the reference data is less impressive and it performed only slightly better than a random selection from the possible output classes. Difficulties in classifying the μ-DS in the time domain are identified from the k-NN results prompting a change to the frequency domain. Processing the μ-DS in the frequency domain permitted the development of an advanced feature extraction routine to maximize the separation of the target classes and therefore reduce the effort required to classify them. The frequency domain also permitted the use of the performance prediction method developed as part of the radar ATR methodology and the introduction of a na¨ıve Bayesian approach to classification. The results for the DTW and k-NN classifiers in the frequency domain were comparable to the time domain, an unexpected result since it was anticipated that the μ-DS would be easier to classify in the frequency domain. However, the naıve Bayesian classifier produced excellent results that matched with the predicted performance suggesting it could not be bettered. With a successful classifier, that would be suitable for real-world use, developed attention turned to the possibilities offered by the multistatic μ-DS. Multiperspective radar ATR uses data collected from different target aspects simultaneously to improve classification rates. It has been demonstrated successful for some of the alternatives to μ-DS based ATR and it was therefore speculated that it might improve the performance of μ-DS ATR solutions. The multiple perspectives required for the classifier were gathered using a multistatic radar developed at University College London (UCL). The production of a dataset, and its subsequent analysis, resulted in the first reported findings in the novel field of the multistatic μ-DS theory. Unfortunately, the nature of the radar used resulted in limited micro-Doppler being observed in the collected data and this reduced its value for classification testing. An attempt to use DTW to perform multiperspective μ-DS ATR was made but the results were inconclusive. However, consideration of the improvements offered by multiperspective processing in alternative forms of ATR mean it is still expected that μ-DS based ATR would benefit from this processing.
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Dilsaver, Benjamin Walter. "Experiments with GMTI Radar using Micro-Doppler." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3678.

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As objects move, their changing shape produces a signature that can be measured by a radar system. That signature is called the micro-Doppler signature. The micro-Doppler signature of an object is a distinguishing characteristic for certain classes of objects. In this thesis features are extracted from the micro-Doppler signature and are used to classify objects. The scope of the objects is limited to humans walking and traveling vehicles. The micro-Doppler features are able to distinguish the two classes of objects. With a sufficient amount of training data, the micro-Doppler features may be used with learning algorithms to predict unknown objects detected by the radar with high accuracy.
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Alzogaiby, Adel. "Using Micro-Doppler radar signals for human gait detection." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86652.

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Thesis (MScEng)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: This work entails the development and performance analysis of a human gait detection system based on radar micro-Doppler signals. The system consists of a tracking functionality and a target classifier. Target micro-Doppler signatures are extracted with Short-Time Fourier Transform (STFT) based spectrogram providing a high-resolution signatures with the radar that is used. A feature extraction mechanism is developed to extract six features from the signature and an artificial neural network (A-NN) based classifier is designed to carry out the classification process. The system is tested on real X-band radar data of human subjects performing six activities. Those activities are walking and speed walking, walking with hands in pockets, marching, running, walking with a weapon, and walking with arms swaying. The multiclass classifier was designed to discriminate between those activities. High classification accuracy of 96% is demonstrated.
AFRIKAANSE OPSOMMING: Hierdie werk behels die ontwikkeling, en analise van werksverrigting, van ’n menslike stapdetekor gebaseer op radar-mikrodoppleranalise. Die stelsel bestaan uit ’n teikenvolger en -klassifiseerder. Die mikrodoppler-kenmerke van ’n teiken word met behulp van die korttyd-Fourier-transform onttrek, en verskaf hoe-resolusie-kenmerke met die radar wat vir die implementering gebruik word. ’n Kenmerkontrekkingstelsel is ontwikkel om ses kenmerke vanuit die spektrogram te onttrek, en ’n kunsmatige neurale netwerk word as klassifiseerder gebruik. Die stelsel is met ’n X-band radar op werklike menslike beweging getoets, terwyl vrywilligers ses aktiwiteite uitgevoer het: loop, loop (hand in die sakke), marsjeer, hardloop, loop met ’n wapen, loop met arms wat swaai. Die multiklas-klassifiseerder is ontwerp om tussen hierdie aktiwiteite te onderskei. ’n Hoe klassifiseringsakkuraatheid van 96% word gedemonstreer.
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Fogle, Orelle Ryan. "Human Micro-Range/Micro-Doppler Signature Extraction, Association, and Statistical Characterization for High-Resolution Radar." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1307733951.

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Ghaleb, Antoine. "Analyse des micro-Doppler de cibles mobiles déformables en imagerie radar." Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00634637.

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Les méthodes traditionnelles de formation d'images ISAR supposent que la cible est rigide et ne tiennent pas compte de ses déformations géométriques. Ces mouvements, qui s'ajoutent au déplacement global de la cible, créent une modulation en fréquence sur le signal réfléchi. Ce phénomène, aussi appelé effet micro-Doppler, se traduit d'un point de vue spectral par un étalement des fréquences autour de la fréquence Doppler centrale. Comme les caractéristiques de ces modulations sont directement reliées aux propriétés géométriques et dynamiques de la cible, l'analyse de l'effet micro-Doppler peut apporter des informations complémentaires aux méthodes existantes de reconnaissance de cibles mobiles. Les travaux précédents ont principalement été consacrés à l'analyse temporelle de l'effet micro-Doppler sans tenir compte de la dimension spatiale. En outre, mis à part les cas d'étude théoriques, il existe très peu de modélisations et de données réelles de cibles déformables. A travers les exemples de la roue et du piéton, cette thèse consiste à caractériser finement les effets des déformations géométriques en imagerie radar, en combinant l'analyse en distance et en Doppler. En outre, un accent est mis sur l'influence de la géométrie relative entre le radar et la cible.\\ Ces travaux s'appuient sur un large volet expérimental où sont exploitées les données issues du radar HYCAM, un système d'acquisition large bande développé par l'ONERA. En complément des mesures, le développement d'un outil de simulation permet de faire le lien entre les données réelles et le modèle de l'objet afin d'extraire des grandeurs physiques du phénomène étudié.
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CIATTAGLIA, Gianluca. "Modern techniques to process micro-Doppler signals from mmWave Radars." Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295142.

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I sistemi radar mmWave stanno diventando molto comuni sui veicoli e le loro capacità, in termini di portata e velocità, li rendono adatti a un'altra classica applicazione radar classica, quella relativa all'effetto micro-Doppler. Dall'elaborazione dei segnali radar mmWave, l'effetto micro-Doppler può essere sfruttato, rendendo così possibile estrarre informazioni interessanti sui bersagli. Con l'enorme larghezza di banda e il breve tempo di trasmissione del segnale, l effetto micro-Doppler può essere utilizzato per diversi scopi come la vibrazione del bersaglio o la classificazione dei bersagli. Grazie anche al progresso delle tecniche di Machine Learning, la loro combinazione con elaborazione del segnale radar è un campo interessante da esplorare e può essere usato per fornire soluzioni a diversi problemi radar. L'effetto Micro-Doppler ha una lunga storia nei sistemi radar, un sacco di letteratura può essere trovata su questo argomento, ma la maggior parte di loro considera dispositivi non commerciali quindi è abbastanza lontano da un caso pratico. In questa dissertazione, diverse tecniche per elaborare i segnali micro-Doppler provenienti da radar automobilistici sarà presentato, con lo scopo di classificarli ed estrarre informazioni sulle vibrazioni dal bersaglio. Il contributo principale di questo lavoro è la proposta di nuove tecniche che possono essere applicato su un sensore commerciale e li rende adatti per il micro- Doppler.
mmWave Radar systems are becoming very common on vehicles and their capabilities, in terms of range and velocity, make them suitable for another classical radar application, the one related to the micro-Doppler effect. From the processing of mmWave radar signals, the micro-Doppler effect can be exploited, making so possible to extract interesting information on the observed targets. With the huge bandwidth and the short signal transmission time, the micro-Doppler effect can be used for different purposes such as target vibration measurements or targets classification. Thanks also to the advance of Machine Learning techniques, their combination with radar signal processing is an interesting field to explore and can be used to provide solutions to different radar problems. The Micro-Doppler effect has a long story in Radar systems, a lot of literature can be found on this topic but most of them consider non-commercial devices so is quite away from a practical case. In this dissertation, different techniques to process the micro-Doppler signals coming from automotive radars will be presented, with the purpose of classifying them and extracting vibration information from the target. The main contribution of this work is the proposal of novel techniques that can be applied on a commercial sensor and makes them suitable for the micro- Doppler application.
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Kizhakkel, Vinit Rajan. "PULSED RADAR TARGET RECOGNITION BASED ON MICRO-DOPPLER SIGNATURES USING WAVELET ANALYSIS." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366033578.

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Clemente, Carmine. "Advanced high resolution methods for radar imaging and micro-Doppler signature extraction." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=18909.

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This Thesis presents radar concepts and signal processing techniques including the fractional Fourier transform (FrFT), Chebyshev polynomial approximation and Singular Spectrum Analysis (SSA) for advanced high resolution radar imaging and micro-Doppler signature extraction. Two novel SAR focussing algorithms in the time-frequency domain are developed using the FrFT. These are called the Fractional Range Doppler Algorithm (FrRDA) and the enhanced Fractional Chirp Scaling Algorithm (eFrCSA. The new methods are tested on simulated and real data sets and are shown to provide higher performances in terms of image quality and resolution than existing frequency domain based methods. The state of the art signal spectrum models of a bistatic point target spectrum for bistatic SAR imaging has been improved by deriving Chebyshev polynomial approximations in place of the conventional Taylor based approximations. This new model increases the accuracy and the efficiency of frequency domain focussing algorithms. Models for micro-Doppler signatures in bistatic SAR are developed and the effect of the different acquisition geometries are considered, including the effect on the final image. A new concept for a Passive Bistatic Radar is introduced for micro-Doppler analysis of helicopters rotor blades. The proposed system exploits the forward scattering enhancement to increase the radar cross section of the helicopters rotor blade allowing an acceptable operative range. The analysis shows how the proposed system could be considered as a good candidate for cheap coast and border control. A detailed analysis on the effect of micro-Doppler from wind turbines and their impact on SAR images is presented. The signal model for such a distributed target is presented and simulation results show how the presence of such a target can significantly decrease and corrupt the image quality. Singular Spectrum Analysis (SSA) is developed for micro-Doppler signature extra ction from SAR clutter and from the direct signal interference and clutter of a passive bistatic radar. The SSA is shown to be robust and capable of performing as a useful tool with the capability of mitigating the effects of clutter on micro-Doppler signatures.
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Cammenga, Zachary Andrew. "High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483438572645656.

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Garry, Joseph Landon. "Imaging Methods for Passive Radar." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500464101265192.

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Books on the topic "Micro-Doppler radar"

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The micro-doppler effect in radar. Boston: Artech House, 2011.

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Griffiths, Hugh, Matthew Ritchie, Francesco Fioranelli, and Alessio Balleri. Micro-Doppler Radar and Its Applications. Institution of Engineering & Technology, 2020.

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Griffiths, Hugh. Micro-Doppler Radar and Its Applications. Institution of Engineering & Technology, 2020.

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Micro-Doppler Characteristics of Radar Targets. Elsevier, 2017. http://dx.doi.org/10.1016/c2015-0-01878-0.

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Fioranelli, Francesco, Hugh Griffiths, Matthew Ritchie, and Alessio Balleri, eds. Micro-Doppler Radar and Its Applications. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/sbra531e.

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Luo, Ying, Qun Zhang, and Yong-an Chen. Micro-Doppler Characteristics of Radar Targets. Elsevier Science & Technology Books, 2016.

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Luo, Ying, Qun Zhang, and Yong-an Chen. Micro-Doppler Characteristics of Radar Targets. Elsevier Science & Technology Books, 2016.

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Chen, Victor C., David Tahmoush, and William J. Miceli, eds. Radar Micro-Doppler Signatures: Processing and Applications. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/pbra034e.

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Radar Micro-Doppler Signatures: Processing and applications. The Institution of Engineering and Technology, 2014.

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Chen, Victor C., William J. Miceli, and David Tahmoush. Radar Micro-Doppler Signatures: Processing and Applications. Institution of Engineering & Technology, 2014.

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Book chapters on the topic "Micro-Doppler radar"

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Hematian, Amirshahram, Yinan Yang, Chao Lu, and Sepideh Yazdani. "Human Motion Analysis and Classification Using Radar Micro-Doppler Signatures." In Software Engineering Research, Management and Applications, 1–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33903-0_1.

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Yessad, Dalila, Abderrahmane Amrouche, Mohamed Debyeche, and Mustapha Djeddou. "Micro-Doppler Classification for Ground Surveillance Radar Using Speech Recognition Tools." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 280–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_33.

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Bauw, Martin, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet, and Olivier Airiau. "Near Out-of-Distribution Detection for Low-Resolution Radar Micro-doppler Signatures." In Machine Learning and Knowledge Discovery in Databases, 384–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26412-2_24.

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Strobach, Edward J., Alan Brewer, Brandi McCarty, and Amanda Makowiecki. "Analysis of Updraft Characteristics from an Airborne Micro-pulsed Doppler Lidar During FIREX-AQ." In Proceedings of the 30th International Laser Radar Conference, 483–89. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37818-8_63.

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Cui, Wen, Chongyi Fan, Xiaotao Huang, and Zhimin Zhou. "Height and Relative Velocity of Pedestrians Estimation Based on Radar Micro-Doppler Signatures." In Lecture Notes in Electrical Engineering, 1269–77. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_152.

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Spinsante, Susanna, Matteo Pepa, Stefano Pirani, Ennio Gambi, and Francesco Fioranelli. "Micro Doppler Radar and Depth Sensor Fusion for Human Activity Monitoring in AAL." In Lecture Notes in Electrical Engineering, 519–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04324-7_62.

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Vandana, A. R., and M. ArokiaSamy. "Comparison of Radar Micro Doppler Signature Analysis Using Short Time Fourier Transform and Discrete Wavelet Packet Transform." In Lecture Notes in Electrical Engineering, 429–38. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9012-9_35.

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Neumann, Christoph, and Tobias Brosch. "Micro-Doppler." In New Methodologies for Understanding Radar Data, 413–36. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbra542e_ch13.

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Ritchie, Matthew, Francesco Fioranelli, and Hugh Griffiths. "Multistatic radar micro-Doppler." In Micro-Doppler Radar and Its Applications, 1–34. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/sbra531e_ch1.

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Chen, Victor C., and William J. Miceli. "Micro-Doppler signatures for sensing micro-motion." In Short-Range Micro-Motion Sensing with Radar Technology, 311–27. Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/pbce125e_ch12.

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Conference papers on the topic "Micro-Doppler radar"

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Smith, Graeme E., Karl Woodbridge, and Chris J. Baker. "Multistatic Micro-Doppler Signature of personnel." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4721060.

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Tahmoush, Dave. "Micro-Range Micro-Doppler for Classification." In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266570.

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Cammenga, Z. A., C. J. Baker, G. E. Smith, and R. Ewing. "Micro-Doppler target scattering." In 2014 IEEE Radar Conference (RadarCon). IEEE, 2014. http://dx.doi.org/10.1109/radar.2014.6875829.

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Kocjancic, Leon, Alessio Balleri, and Thomas Merlet. "Micro-Doppler Signature Extraction with Multibeam Radar." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171364.

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Smith, Graeme E., Karl Woodbridge, and Chris J. Baker. "Micro-Doppler Signature Classification." In 2006 CIE International Conference on Radar. IEEE, 2006. http://dx.doi.org/10.1109/icr.2006.343175.

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Gray, Douglas, Brian Ng, and Si Tran Nguyen. "Micro-Doppler Power Analysis for Drone Discrimination." In 2023 IEEE International Radar Conference (RADAR). IEEE, 2023. http://dx.doi.org/10.1109/radar54928.2023.10371159.

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Tahmoush, Dave. "Wideband radar micro-doppler applications." In SPIE Defense, Security, and Sensing, edited by G. Charmaine Gilbreath and Chadwick Todd Hawley. SPIE, 2013. http://dx.doi.org/10.1117/12.2015474.

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Yang, Le, Gang Li, Matthew Ritchie, Francesco Fioranelli, and Hugh Griffiths. "Gait classification based on micro-Doppler features." In 2016 CIE International Conference on Radar (RADAR). IEEE, 2016. http://dx.doi.org/10.1109/radar.2016.8059301.

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Ghaleb, A., L. Vignaud, and J. M. Nicolas. "Micro-Doppler analysis of pedestrians in ISAR imaging." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4720889.

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Yang, Yang, Chunping Hou, Yue Lang, and Chao Li. "Motion Classification Based on Noisy Micro-Doppler Signatures." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171374.

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Reports on the topic "Micro-Doppler radar"

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, September 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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Abstract:
In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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