Auswahl der wissenschaftlichen Literatur zum Thema „Micro-Doppler radar“
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Zeitschriftenartikel zum Thema "Micro-Doppler radar"
Gong, Jiangkun, Jun Yan, Deren Li und Deyong Kong. „Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times“. Drones 6, Nr. 9 (19.09.2022): 262. http://dx.doi.org/10.3390/drones6090262.
Der volle Inhalt der QuelleKrasnov, Oleg A., und 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, Nr. 3-4 (Juni 2015): 459–67. http://dx.doi.org/10.1017/s1759078715000641.
Der volle Inhalt der QuelleHassan, Shahid, Xiangrong Wang, Saima Ishtiaq, Nasim Ullah, Alsharef Mohammad und Abdulfattah Noorwali. „Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar“. Remote Sensing 15, Nr. 7 (24.03.2023): 1752. http://dx.doi.org/10.3390/rs15071752.
Der volle Inhalt der QuelleWang, Zhihao, Ying Luo, Kaiming Li, Hang Yuan und Qun Zhang. „Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna“. Remote Sensing 14, Nr. 11 (26.05.2022): 2549. http://dx.doi.org/10.3390/rs14112549.
Der volle Inhalt der QuelleZang, Bo, Mingzhe Zhu, Xianda Zhou, Lu Zhong und Zijiao Tian. „Application of S-Transform Random Consistency in Inverse Synthetic Aperture Imaging Laser Radar Imaging“. Applied Sciences 9, Nr. 11 (05.06.2019): 2313. http://dx.doi.org/10.3390/app9112313.
Der volle Inhalt der QuelleSingh, Ashish Kumar, und Yong-Hoon Kim. „Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN“. Traitement du Signal 38, Nr. 4 (31.08.2021): 1033–39. http://dx.doi.org/10.18280/ts.380413.
Der volle Inhalt der QuelleHarsh, Archit. „Measuring Radar Signatures of a Simple Pendulum using Cantenna Radar“. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, Nr. 5 (13.04.2016): 6785–95. http://dx.doi.org/10.24297/ijct.v15i5.1653.
Der volle Inhalt der QuelleDing, Yipeng, Chengxi Lei, Xuemei Xu, Kehui Sun und 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.
Der volle Inhalt der QuelleMolchanov, Pavlo, Ronny I. A. Harmanny, Jaco J. M. de Wit, Karen Egiazarian und Jaakko Astola. „Classification of small UAVs and birds by micro-Doppler signatures“. International Journal of Microwave and Wireless Technologies 6, Nr. 3-4 (19.03.2014): 435–44. http://dx.doi.org/10.1017/s1759078714000282.
Der volle Inhalt der QuelleCui, Rui, Ai Guo Sheng, Ji Fei Pan, Bing He und Jing Zhu. „Research on Jamming Method of False Target Based on Micro-Motion Modulation“. Applied Mechanics and Materials 556-562 (Mai 2014): 2707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2707.
Der volle Inhalt der QuelleDissertationen zum Thema "Micro-Doppler radar"
Smith, G. E. „Radar target micro-Doppler signature classification“. Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/18688/.
Der volle Inhalt der QuelleDilsaver, Benjamin Walter. „Experiments with GMTI Radar using Micro-Doppler“. BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3678.
Der volle Inhalt der QuelleAlzogaiby, Adel. „Using Micro-Doppler radar signals for human gait detection“. Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86652.
Der volle Inhalt der QuelleENGLISH 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.
Der volle Inhalt der QuelleGhaleb, Antoine. „Analyse des micro-Doppler de cibles mobiles déformables en imagerie radar“. Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00634637.
Der volle Inhalt der QuelleCIATTAGLIA, Gianluca. „Modern techniques to process micro-Doppler signals from mmWave Radars“. Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295142.
Der volle Inhalt der QuellemmWave 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.
Der volle Inhalt der QuelleClemente, 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.
Der volle Inhalt der QuelleCammenga, 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.
Der volle Inhalt der QuelleGarry, Joseph Landon. „Imaging Methods for Passive Radar“. The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500464101265192.
Der volle Inhalt der QuelleBücher zum Thema "Micro-Doppler radar"
The micro-doppler effect in radar. Boston: Artech House, 2011.
Den vollen Inhalt der Quelle findenGriffiths, Hugh, Matthew Ritchie, Francesco Fioranelli und Alessio Balleri. Micro-Doppler Radar and Its Applications. Institution of Engineering & Technology, 2020.
Den vollen Inhalt der Quelle findenGriffiths, Hugh. Micro-Doppler Radar and Its Applications. Institution of Engineering & Technology, 2020.
Den vollen Inhalt der Quelle findenMicro-Doppler Characteristics of Radar Targets. Elsevier, 2017. http://dx.doi.org/10.1016/c2015-0-01878-0.
Der volle Inhalt der QuelleFioranelli, Francesco, Hugh Griffiths, Matthew Ritchie und Alessio Balleri, Hrsg. Micro-Doppler Radar and Its Applications. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/sbra531e.
Der volle Inhalt der QuelleLuo, Ying, Qun Zhang und Yong-an Chen. Micro-Doppler Characteristics of Radar Targets. Elsevier Science & Technology Books, 2016.
Den vollen Inhalt der Quelle findenLuo, Ying, Qun Zhang und Yong-an Chen. Micro-Doppler Characteristics of Radar Targets. Elsevier Science & Technology Books, 2016.
Den vollen Inhalt der Quelle findenChen, Victor C., David Tahmoush und William J. Miceli, Hrsg. Radar Micro-Doppler Signatures: Processing and Applications. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/pbra034e.
Der volle Inhalt der QuelleRadar Micro-Doppler Signatures: Processing and applications. The Institution of Engineering and Technology, 2014.
Den vollen Inhalt der Quelle findenChen, Victor C., William J. Miceli und David Tahmoush. Radar Micro-Doppler Signatures: Processing and Applications. Institution of Engineering & Technology, 2014.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Micro-Doppler radar"
Hematian, Amirshahram, Yinan Yang, Chao Lu und 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.
Der volle Inhalt der QuelleYessad, Dalila, Abderrahmane Amrouche, Mohamed Debyeche und 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.
Der volle Inhalt der QuelleBauw, Martin, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet und 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.
Der volle Inhalt der QuelleStrobach, Edward J., Alan Brewer, Brandi McCarty und 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.
Der volle Inhalt der QuelleCui, Wen, Chongyi Fan, Xiaotao Huang und 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.
Der volle Inhalt der QuelleSpinsante, Susanna, Matteo Pepa, Stefano Pirani, Ennio Gambi und 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.
Der volle Inhalt der QuelleVandana, A. R., und 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.
Der volle Inhalt der QuelleNeumann, Christoph, und 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.
Der volle Inhalt der QuelleRitchie, Matthew, Francesco Fioranelli und 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.
Der volle Inhalt der QuelleChen, Victor C., und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Micro-Doppler radar"
Smith, Graeme E., Karl Woodbridge und 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.
Der volle Inhalt der QuelleTahmoush, Dave. „Micro-Range Micro-Doppler for Classification“. In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266570.
Der volle Inhalt der QuelleCammenga, Z. A., C. J. Baker, G. E. Smith und R. Ewing. „Micro-Doppler target scattering“. In 2014 IEEE Radar Conference (RadarCon). IEEE, 2014. http://dx.doi.org/10.1109/radar.2014.6875829.
Der volle Inhalt der QuelleKocjancic, Leon, Alessio Balleri und 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.
Der volle Inhalt der QuelleSmith, Graeme E., Karl Woodbridge und 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.
Der volle Inhalt der QuelleGray, Douglas, Brian Ng und 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.
Der volle Inhalt der QuelleTahmoush, Dave. „Wideband radar micro-doppler applications“. In SPIE Defense, Security, and Sensing, herausgegeben von G. Charmaine Gilbreath und Chadwick Todd Hawley. SPIE, 2013. http://dx.doi.org/10.1117/12.2015474.
Der volle Inhalt der QuelleYang, Le, Gang Li, Matthew Ritchie, Francesco Fioranelli und 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.
Der volle Inhalt der QuelleGhaleb, A., L. Vignaud und 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.
Der volle Inhalt der QuelleYang, Yang, Chunping Hou, Yue Lang und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Micro-Doppler radar"
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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