Relevant bibliographies by topics / Micro-Doppler, Radar, FMCW, Machine Learning

Contents

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

Academic literature on the topic 'Micro-Doppler, Radar, FMCW, Machine Learning'

Author: Grafiati
Published: 20 February 2023
Last updated: 31 July 2025

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Journal articles on the topic "Micro-Doppler, Radar, FMCW, Machine Learning"

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Zhang, Yuan, Haotian Tang, Ye Wu, Bolun Wang, and Dalin Yang. "FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks." Sensors 24, no. 14 (2024): 4570. http://dx.doi.org/10.3390/s24144570.

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Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted
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Hyun, Eugin, and YoungSeok Jin. "Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor." Sensors 20, no. 7 (2020): 2001. http://dx.doi.org/10.3390/s20072001.

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In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a rea
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Shah, Syed Aziz, Ahsen Tahir, Julien Le Kernec, Ahmed Zoha, and Francesco Fioranelli. "Data portability for activities of daily living and fall detection in different environments using radar micro-doppler." Neural Computing and Applications 34, no. 10 (2022): 7933–53. http://dx.doi.org/10.1007/s00521-022-06886-2.

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AbstractThe health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform asses
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Jhaung, Yu-Chiao, Yu-Ming Lin, Chiao Zha, Jenq-Shiou Leu, and Mario Köppen. "Implementing a Hand Gesture Recognition System Based on Range-Doppler Map." Sensors 22, no. 11 (2022): 4260. http://dx.doi.org/10.3390/s22114260.

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There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range
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Benyahia, Zakaria, Mostafa Hefnawi, Mohamed Aboulfatah, Hassan Abdelmounim, and Taoufiq Gadi. "A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems." ITM Web of Conferences 48 (2022): 01010. http://dx.doi.org/10.1051/itmconf/20224801010.

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This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close
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Zhao, Yanhua, Vladica Sark, Milos Krstic, and Eckhard Grass. "Low Complexity Radar Gesture Recognition Using Synthetic Training Data." Sensors 23, no. 1 (2022): 308. http://dx.doi.org/10.3390/s23010308.

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Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computation
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Cha, Daewoong, Sohee Jeong, Minwoo Yoo, Jiyong Oh, and Dongseog Han. "Multi-Input Deep Learning Based FMCW Radar Signal Classification." Electronics 10, no. 10 (2021): 1144. http://dx.doi.org/10.3390/electronics10101144.

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In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated co
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Tsang, Ing Jyh, Federico Corradi, Manolis Sifalakis, Werner Van Leekwijck, and Steven Latré. "Radar-Based Hand Gesture Recognition Using Spiking Neural Networks." Electronics 10, no. 12 (2021): 1405. http://dx.doi.org/10.3390/electronics10121405.

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We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector ma
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Jiang, Liubing, Minyang Wu, Li Che, Xiaoyong Xu, Yujie Mu, and Yongman Wu. "Continuous Human Motion Recognition Based on FMCW Radar and Transformer." Journal of Sensors 2023 (January 24, 2023): 1–14. http://dx.doi.org/10.1155/2023/2951812.

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Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements, it can also only be recognized as a single movement. Another point that cannot be overlooked is that continuous action recognition methods are able to recognize continuously changing actions but ignore the issue of whether
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Souhila Korti, Djazila, and Zohra Slimane. "Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures." International journal of electrical and computer engineering systems 14, no. 8 (2023): 893–902. http://dx.doi.org/10.32985/ijeces.14.8.7.

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Developing accurate classification models for radar-based Human Activity Recognition (HAR), capable of solving real-world problems, depends heavily on the amount of available data. In this paper, we propose a simple, effective, and generalizable data augmentation strategy along with preprocessing for micro-Doppler signatures to enhance recognition performance. By leveraging the decomposition properties of the Discrete Wavelet Transform (DWT), new samples are generated with distinct characteristics that do not overlap with those of the original samples. The micro-Doppler signatures are projecte
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Dissertations / Theses on the topic "Micro-Doppler, Radar, FMCW, Machine Learning"

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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 bersagl
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Brooks, Daniel. "Deep Learning and Information Geometry for Time-Series Classification." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS276.

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L’apprentissage automatique, et en particulier l’apprentissage profond, unit un arsenal d’outillages puissants pour modeler et étudier les distributions statistiques sous-jacentes aux données, permettant ainsi l’extraction d’informations sémantiquement valides et interprétables depuis des séquences tabulaires de nombres par ailleurs indigestes à l’œil humain. Bien que l’apprentissage fournisse une solution générique à la plupart des problèmes, certains types de données présentent une riche structure issue de phénomènes physiques: les images ont la localité spatiale, les sons la séquentialité t
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Book chapters on the topic "Micro-Doppler, Radar, FMCW, Machine Learning"

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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. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26412-2_24.

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Fei, Tai. "Automotive Radar Systems: Architecture, Signal Processing, and Future Perspectives." In Vehicle Technology and Automotive Engineering [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008976.

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This chapter provides an overview of automotive radar technology, emphasizing its role in advanced driver-assistance systems (ADAS) through real-time measurements of range, angle, and velocity. We summarize the evolution of radar waveforms—such as frequency-modulated continuous wave (FMCW), phase-modulated continuous wave (PMCW), orthogonal frequency division multiplexing (OFDM), and phase-coded-FMCW (PC-FMCW)—and their signal-processing techniques for range-Doppler estimation and angle-of-arrival determination. Challenges in urban scenarios, such as the need for 4D sensing (range, velocity, a
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Conference papers on the topic "Micro-Doppler, Radar, FMCW, Machine Learning"

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Singh, Nirbhay Kumar, Malwinder Singh, Shaik Abdul Subhan, and Seema. "Phase Noise Effects on Micro-Doppler Extraction for Radar Target Classification Using Machine Learning." In 2024 Asia-Pacific Microwave Conference (APMC). IEEE, 2024. https://doi.org/10.1109/apmc60911.2024.10867536.

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Bernard-Cooper, Joshua, Samiur Rahman, and Duncan A. Robertson. "Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data." In Radar Sensor Technology XXVI, edited by Ann M. Raynal and Kenneth I. Ranney. SPIE, 2022. http://dx.doi.org/10.1117/12.2618026.

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Jibrin, Fahad Abdu, Abdulrazaq Abdulaziz, Abubakar Sadiq Muhammad, A. D. Usman, and Yusuf Jibril. "Indoor Human Activity Classification Based on FMCW Radar Micro-Doppler Signatures and Deep-Learning Networks." In 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). IEEE, 2021. http://dx.doi.org/10.1109/icmeas52683.2021.9692418.

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Barbaresco, Frederic, Daniel Brooks, and Claude Adnet. "Machine and Deep Learning for Drone Radar Recognition by Micro-Doppler and Kinematic criteria." In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266371.

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Barbaresco, Frederic. "Radar Micro-Doppler Signal Encoding in Siegel Unit Poly-Disk for Machine Learning in Fisher Metric Space." In 2018 19th International Radar Symposium (IRS). IEEE, 2018. http://dx.doi.org/10.23919/irs.2018.8448021.

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Reports on the topic "Micro-Doppler, Radar, FMCW, Machine Learning"

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

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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 Ass
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Journal articles
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