Searched for: author%3A%22Fioranelli%2C+F.%22
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Shrestha, Aman (author), Li, Haobo (author), Kernec, Julien le (author), Fioranelli, F. (author)
Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic...
journal article 2020
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Turpin, Alex (author), Musarra, Gabriella (author), Kapitany, Valentin (author), Tonolini, Francesco (author), Lyons, Ashley (author), Starshynov, Ilya (author), Villa, Federica (author), Conca, Enrico (author), Fioranelli, F. (author)
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial structure in the detector or illumination, i.e., imaging with just a single-point sensor, would require solving a very strongly ill-posed inverse retrieval problem...
journal article 2020
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Gurbuz, Sevgi Z. (author), Rahman, M. Mahbubur (author), Kurtoglu, Emre (author), Macks, Trevor (author), Fioranelli, F. (author)
Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature...
journal article 2020
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Li, Haobo (author), Le Kernec, Julien (author), Mehul, Ajay (author), Fioranelli, F. (author)
This paper discusses a fusion framework with data from multiple, distributed radar sensors based on conventional classifiers, and transfer learning with pre-trained deep networks. The application considered is the classification of gait styles and the detection of critical accidents such as falls. The data were collected from a network comprised...
conference paper 2020
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Yun, Joongsup (author), Anderson, David (author), Fioranelli, F. (author)
This paper presents parametric investigation results on a staring FMCW radar system which targets drone swarms. The parametric investigation has been carried out by using the RAPID-SIM which facilitates system-level analysis of drone swarms' radar signatures. This paper explains concepts of the simulator's each module and also covers two...
conference paper 2020
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Bennet, Cameron (author), Jahangir, Mohammad (author), Fioranelli, F. (author), Ahmad, Bashar I (author), Le Kernec, Julien (author)
The commercialization of drones has granted the public with unprecedented access to unmanned aviation. As such, the detection, tracking, and classification of drones in radars have become an area in high demand to mitigate accidental or voluntary misuse of these platforms. This paper focuses on the classification of drone targets in a safety...
conference paper 2020
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Li, X. (author), Li, Zhenghui (author), Fioranelli, F. (author), Yang, Shufan (author), Romain, Olivier (author), Le Kernec, Julien (author)
Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in...
journal article 2020
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Li, X. (author), He, Y. (author), Fioranelli, F. (author), Jing, X. (author), Yarovoy, Alexander (author), Yang, Y. (author)
The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD)...
journal article 2020
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Li, H. (author), Mehul, A. (author), Kernec, J. Le (author), Gurbuz, S. Z. (author), Fioranelli, F. (author)
This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those...
journal article 2020
Searched for: author%3A%22Fioranelli%2C+F.%22
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