Teses / dissertações sobre o tema "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/.
Texto completo da fonteDilsaver, Benjamin Walter. "Experiments with GMTI Radar using Micro-Doppler". BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3678.
Texto completo da fonteAlzogaiby, Adel. "Using Micro-Doppler radar signals for human gait detection". Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86652.
Texto completo da fonteENGLISH 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.
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.
Texto completo da fonteGhaleb, Antoine. "Analyse des micro-Doppler de cibles mobiles déformables en imagerie radar". Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00634637.
Texto completo da fonteCIATTAGLIA, Gianluca. "Modern techniques to process micro-Doppler signals from mmWave Radars". Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295142.
Texto completo da fontemmWave 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.
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.
Texto completo da fonteClemente, 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.
Texto completo da fonteCammenga, 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.
Texto completo da fonteGarry, Joseph Landon. "Imaging Methods for Passive Radar". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500464101265192.
Texto completo da fonteMerelle, Vincent. "Concept de radars novateurs pour la vision à travers les milieux opaques". Thesis, La Rochelle, 2018. http://www.theses.fr/2018LAROS017/document.
Texto completo da fonte"Vision" through opaque environments (walls, partitions, rubble, or any environment that obscures human vision) is one of the key issues of control and security. Advances on this issue have led to operational shortrange radar systems for people detection and tracking in simple environments. However, most of them require the targets to move in order to differentiate them from static objects. This requirement constitues a major shortcoming for a certain number of real scenarios where people, by strategies or by constraints, remain motionless. Hence, this thesis aims to explore the mechanisms of detection of static people through their micro-movements, e.g. movements induced by the thorax during breathing. We have studied - from a theoretical point of view - the physical principles underlying the detection of these micro-movements by pulsed UWB radar with the pulsed Doppler phenomenon, which relies on consecutive measurements of the reflected pulses phases. The understanding of this phenomenon made it possible to define a radar architecture and to position it, in terms of contributions, with regard to the different UWB radars proposed in the literature : the FMCW and the noise radar. Two radar devices served as support for this work. An academic demonstrator based on the use of a fast oscilloscope to digitize the pulses. It allowed to set up a complete processing chain for the application of vision through the walls. The second device is a radar prototype developed around a high-speed scanning platform (100 Gsps perequivalent sampling) with a very high refresh rate (100 Hz). This prototype is built around an FPGA, a fast ADC (1.25 GHz) and a very wide band T&H (18 GHz). This thereby enables to detect micro-movements by pulsed Doppler processing
Belgiovane, Domenic John Jr. "Advancing Millimeter-Wave Vehicular Radar Test Targets for Automatic Emergency Braking (AEB) Sensor Evaluation". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511867574425366.
Texto completo da fonteBrooks, Daniel. "Deep Learning and Information Geometry for Time-Series Classification". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS276.
Texto completo da fonteMachine Learning, and in particular Deep Learning, is a powerful tool to model and study the intrinsic statistical foundations of data, allowing the extraction of meaningful, human-interpretable information from otherwise unpalatable arrays of floating points. While it provides a generic solution to many problems, some particular data types exhibit strong underlying physical structure: images have spatial locality, audio has temporal sequentiality, radar has time-frequency structure. Both intuitively and formally, there can be much to gain in leveraging this structure by adapting the subsequent learning models. As convolutional architectures for images, signal properties can be encoded and harnessed within the network. Conceptually, this would allow for a more intrinsic handling of the data, potentially leading to more efficient learning models. Thus, we will aim to use known structures in the signals as model priors. Specifically, we build dedicated deep temporal architectures for time series classification, and explore the use of complex values in neural networks to further refine the analysis of structured data. Going even further, one may wish to directly study the signal’s underlying statistical process. As such, Gaussian families constitute a popular candidate. Formally, the covariance of the data fully characterizes such a distribution; developing Machine Learning algorithms on covariance matrices will thus be a central theme throughout this thesis. Statistical distributions inherently diverge from the Euclidean framework; as such, it is necessary to study them on the appropriate, curved Riemannian manifold, as opposed to a flat, Euclidean space. Specifically, we contribute to existing deep architectures by adding normalizations in the form of data-aware mappings, and a Riemannian Batch Normalization algorithm. We showcase empirical validation through a variety of different tasks, including emotion and action recognition from video and Motion Capture data, with a sharpened focus on micro-Doppler radar data for Non-Cooperative Target Recognition drone recognition. Finally, we develop a library for the Deep Learning framework PyTorch, to spur reproducibility and ease of use
Boisot, Olivier. "Étude de la rétrodiffusion des surfaces d'eau en bande Ka à faible incidence". Electronic Thesis or Diss., Toulon, 2015. http://www.theses.fr/2015TOUL0008.
Texto completo da fonteThe evolution of the altimetric techniques from Ku-band to Ka-band and the wide swath al-timetry in the context of the SWOT mission (« Surface Water Ocean Topography », CNES/NASA) raises new scientific questions about the validity of the backscattering models from water sur-faces in such a frequency band and errors in estimating water heights from time-evolving water surfaces. A backscattering model (GO4) adapted to the SWOT configuration is introduced. It preserves the accuracy of the referencial Physical Optics model while maintaining the simplicity of the clas-sical Optical Geometrics model. In addition to the classical slope parameter, it introduces another parameter called as « effective curvature » (msc). This model allows the inverson of the surface slope and curvature parameters under certain conditions which are developped in this manus-cript. The joint validity of the backscattering models in Ka-band and from water surfaces is che-cked from controlled wind-wave tank radar measurements . In a last part, the temporal properties of the backscattered signal is studied, in particular the correlation time and the Doppler shift induced by waves motion. Influence of the latters on the non focused SAR synthesis is studied in the context of the SWOT system
Boisot, Olivier. "Étude de la rétrodiffusion des surfaces d'eau en bande Ka à faible incidence". Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0008/document.
Texto completo da fonteThe evolution of the altimetric techniques from Ku-band to Ka-band and the wide swath al-timetry in the context of the SWOT mission (« Surface Water Ocean Topography », CNES/NASA) raises new scientific questions about the validity of the backscattering models from water sur-faces in such a frequency band and errors in estimating water heights from time-evolving water surfaces. A backscattering model (GO4) adapted to the SWOT configuration is introduced. It preserves the accuracy of the referencial Physical Optics model while maintaining the simplicity of the clas-sical Optical Geometrics model. In addition to the classical slope parameter, it introduces another parameter called as « effective curvature » (msc). This model allows the inverson of the surface slope and curvature parameters under certain conditions which are developped in this manus-cript. The joint validity of the backscattering models in Ka-band and from water surfaces is che-cked from controlled wind-wave tank radar measurements . In a last part, the temporal properties of the backscattered signal is studied, in particular the correlation time and the Doppler shift induced by waves motion. Influence of the latters on the non focused SAR synthesis is studied in the context of the SWOT system
"Terahertz Micro-Doppler Radar for Detection and Characterization of Multicopters". Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50543.
Texto completo da fonteDissertation/Thesis
Masters Thesis Electrical Engineering 2018
Björklund, Svante. "Signal Processing for Radar with Array Antennas and for Radar with Micro-Doppler Measurements". Doctoral thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13639.
Texto completo da fonteAnderson, Michael Glen 1979. "Design of multiple frequency continuous wave radar hardware and micro-Doppler based detection and classification algorithms". Thesis, 2008. http://hdl.handle.net/2152/4000.
Texto completo da fontetext
Coppola, Rudi. "Road Users Classification Based on Bi-Frame Micro-Doppler with 24-GHz FMCW Radar". Thesis, 2021. http://hdl.handle.net/10754/668953.
Texto completo da fonteNieh, Chi-hsuan, e 聶啓軒. "Analysis of Micro-Doppler Effect of Human Gait by Through-Wall Continuous-Wave Radar". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/34608152096274529552.
Texto completo da fonte國立中央大學
遙測科技碩士學位學程
102
Radar Doppler has various applications in both military and civil aspect. The target moving parameters such as moving direction, moving velocity and so on can be well estimated through the analysis of radar Doppler effects. A new research called micro-Doppler which is proposed by the famous scholar Victor C. Chen was developed on the basis of micro motion model of objects. Since the human beings and the other creatures usually walk or run with a constant velocity, stride and gait frequency, there are some micro-Doppler signatures on their spectrums. Human gait detection and characteristic extraction mainly utilize the human’s walking dynamic features to modulate the radar echo phase, use time-frequency analysis method to obtain the spectrum, and extract the micro-Doppler signature of human gait from spectrum. Through wall surveillance (TWS) is a kind of detection technology which can penetrate walls and other obstacles. With the ability of through-obstacle detection, TWS has been playing an important role in military reconnaissance, counter-terrorism, hostage rescue, earthquake casualty search and so on. In this study, the micro-Doppler effect of the vibration and rotation is firstly discussed which is depend on the specific micro motion, and followed by the tool of time-frequency analysis. A gait echo model is proposed, which represents the rotation angles, displacements and relative phase relationship of joints, scatterers and the instantaneous Radar Cross Section (RCS). Finally, micro Doppler is combined with TWS for the feasibility of human gait direction determination.