Searched for: subject%3A%22Micro%255C-Doppler%22
(1 - 20 of 41)

Pages

document
Pappas, Apostolos (author)
<br/>
master thesis 2023
document
van Nifterik, Bob (author)
Radar micro-Doppler signatures are powerful indicators of target movements and activities, enabling the extraction of valuable information about various objects' internal and external dynamics. Consequently, classifying these signatures has become crucial in numerous applications, ranging from target recognition in surveillance, to biomedical...
master thesis 2023
document
Focante, Edoardo (author)
In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to...
master thesis 2023
document
Focante, E. (author), Martin, Lilian De (author), Coutino, Mario (author)
In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available...
conference paper 2023
document
Corradi, Federico (author), Fioranelli, F. (author)
The advent of consumer and industrial Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has opened business opportunities in many fields, including logistics, smart agriculture, inspection, surveillance, and construction. In addition, the autonomous operations of UAVs reduce risks by minimizing the time spent by human workers...
conference paper 2022
document
Zhu, S. (author), Guendel, Ronny (author), Yarovoy, Alexander (author), Fioranelli, F. (author)
Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for spatial–temporal pattern extraction is proposed. The 2-D CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the...
journal article 2022
document
Guendel, Ronny (author), Fioranelli, F. (author), Yarovoy, Alexander (author)
Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated in this paper using a network of five spatially distributed pulsed Ultra-Wideband radars. While activities performed continuously and in unconstrained trajectories provide a more realistic and natural scenario for HAR, the network of radar sensors is proposed...
journal article 2022
document
Guendel, Ronny (author), Fioranelli, F. (author), Yarovoy, Alexander (author)
Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated using 5 spatially distributed pulsed Ultra-Wideband (UWB) radars. Such activities performed in arbitrary and unconstrained trajectories render a more natural occurrence of Activities of Daily Living (ADL) to be recognized. An innovative signal level fusion method...
conference paper 2022
document
Yang, Ximei (author), Guendel, Ronny (author), Yarovoy, Alexander (author), Fioranelli, F. (author)
Human activities classification in assisted living is one of the emerging applications of radar. The conventional analysis considers micro-Doppler signatures as the chosen input for feature extraction or deep learning classification algorithms, or, less frequently, other radar data formats such as the range-time, the range-Doppler, or the...
conference paper 2022
document
Li, Xinyu (author), He, Yuan (author), Fioranelli, F. (author), Jing, Xiaojun (author)
Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels...
journal article 2022
document
Svenningsson, P.O. (author), Fioranelli, F. (author), Yarovoy, Alexander (author), Martone, Anthony F. (author)
In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures...
journal article 2022
document
Yang, Yang (author), Yang, Xiaoyi (author), Sakamoto, Takuya (author), Fioranelli, F. (author), Li, Beichen (author), Lang, Yue (author)
In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably...
journal article 2022
document
Yang, Kai (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)
Radar is now widely used in human activity classification because of its contactless sensing capabilities, robustness to light conditions and privacy preservation compared to plain optical images. It has great value in elderly care, monitoring accidental falls and abnormal behaviours. Monostatic radar suffers from degradation in performance...
conference paper 2022
document
Zhang, Xinyu (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)
Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler...
conference paper 2022
document
Svenningsson, P.O. (author), Fioranelli, F. (author), Yarovoy, Alexander (author)
In this paper, the classification of human activity from micro-Doppler spectrograms measured by a radar network is considered. To cope with differences between the training and test datasets due to changes in the set of participants, signal-to-noise ratio and polarimetry, domain adaptation is proposed. To realize this, linear mapping between the...
conference paper 2021
document
Ji, Haoran (author), Hou, Chunping (author), Yang, Yang (author), Fioranelli, F. (author), Lang, Yue (author)
In this letter, a radar-based gait authentication method is proposed. We focus on the overfitting problem on the target category caused by limited training data in authentication models and propose a one-class classification model to alleviate this problem. The effectiveness of such model is verified by establishing a radar-based gait dataset,...
journal article 2021
document
Pallotta, Luca (author), Cauli, Michela (author), Clemente, Carmine (author), Fioranelli, F. (author), Giunta, Gaetano (author), Farina, Alfonso (author)
In this paper a method capable of automatically classify radar signals of human hand-gestures exploiting the micro-Doppler signature is designed. In particular, the methodology focuses on the extraction of the Chebyshev moments from the cadence velocity diagram (CVD) of each recorded signal. The algorithm benefits from interesting properties of...
conference paper 2021
document
Li, Zhenghui (author), Le Kernec, Julien (author), Fioranelli, F. (author), Romain, Olivier (author), Zhang, Lei (author), Yang, Shufan (author)
In personnel recognition based on radar, significant research exists on statistical features extracted from the micro-Doppler signatures, whereas research considering other domains and information such as phase is less developed. This paper presents the use of deep learning methods to integrate both phase and magnitude features from range...
conference paper 2021
document
Guendel, Ronny (author), Unterhorst, M. (author), Gambi, Ennio (author), Fioranelli, F. (author), Yarovoy, Alexander (author)
Continuous Activities of Daily Living (ADL) recognition in an arbitrary movement direction using five distributed pulsed Ultra-Wideband (UWB) radars in a coordinated network is proposed. Classification approaches in unconstrained activity trajectories that render a more natural occurrence for Human Activity Recognition (HAR) are investigated....
conference paper 2021
document
Papanastasiou, V. S. (author), Trommel, R. P. (author), Harmanny, R. I.A. (author), Yarovoy, Alexander (author)
For the first time identification of human individuals using micro-Doppler (m-D) features measured at X-band has been demonstrated. Deep Convolutional Neural Networks (DCNNs) have been used to perform classification. Inspection and visualization of the classification results were performed using Uniform Manifold Approximation and Projection ...
conference paper 2021
Searched for: subject%3A%22Micro%255C-Doppler%22
(1 - 20 of 41)

Pages