780 research outputs found
Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology
This paper discusses the challenges of detecting and categorizing small
drones with radar automatic target recognition (ATR) technology. The authors
suggest integrating ATR capabilities into drone detection radar systems to
improve performance and manage emerging threats. The study focuses primarily on
drones in Group 1 and 2. The paper highlights the need to consider kinetic
features and signal signatures, such as micro-Doppler, in ATR techniques to
efficiently recognize small drones. The authors also present a comprehensive
drone detection radar system design that balances detection and tracking
requirements, incorporating parameter adjustment based on scattering region
theory. They offer an example of a performance improvement achieved using
feedback and situational awareness mechanisms with the integrated ATR
capabilities. Furthermore, the paper examines challenges related to one-way
attack drones and explores the potential of cognitive radar as a solution. The
integration of ATR capabilities transforms a 3D radar system into a 4D radar
system, resulting in improved drone detection performance. These advancements
are useful in military, civilian, and commercial applications, and ongoing
research and development efforts are essential to keep radar systems effective
and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Solutions for Sustainable Economic Development - 4th Arctic Science Ministerial Meeting Report
Arctic Science Ministerial is a unique form of scientific cooperation that traditionally advocates preserving the Arctic region as a territory of peace, stability and constructive interaction focused on achieving concrete, practical results in the interests of all people in the northern latitudes, including indigenous peoples.
The Russian Federation continues the coordinating functions within the ASM adopted from previous coordinators on June 16, 2021 at the final ASM3 webinar, and on October 14, 2021 in Reykjavik, Iceland at the annual international Arctic Circle Assembly, based on the continuity of previous ASM and the increasing relevance of scientific research in the Arctic.
This book provides an overview of past events - webinars, participation in conference roundtables - with the aim of sharing scientific experience of Arctic research and forming informational materials to support science and higher education activities through international organizations and forums in the Arctic zone, supporting and updating the database of Arctic research projects carried out by scientific and educational organizations, including jointly, as well as through international.
The information base for this work was the results of feedback assessment from Russian and foreign scientific and educational organizations, data on international projects in the Arctic, materials from the websites of the Arctic Council https://arctic-council.org/ and the working groups of the Arctic Council. In addition, climate, geological, biological, sociological, and technological research was used as the basis for developing strategies for sustainable economic development in the Arctic that take into account the interests of all stakeholders, including indigenous peoples, environmental organizations, industry, and government agencies
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems
This paper presents a brief examination of Automatic Target Recognition (ATR)
technology within ground-based radar systems. It offers a lucid comprehension
of the ATR concept, delves into its historical milestones, and categorizes ATR
methods according to different scattering regions. By incorporating ATR
solutions into radar systems, this study demonstrates the expansion of radar
detection ranges and the enhancement of tracking capabilities, leading to
superior situational awareness. Drawing insights from the Russo-Ukrainian War,
the paper highlights three pressing radar applications that urgently
necessitate ATR technology: detecting stealth aircraft, countering small
drones, and implementing anti-jamming measures. Anticipating the next wave of
radar ATR research, the study predicts a surge in cognitive radar and machine
learning (ML)-driven algorithms. These emerging methodologies aspire to
confront challenges associated with system adaptation, real-time recognition,
and environmental adaptability. Ultimately, ATR stands poised to revolutionize
conventional radar systems, ushering in an era of 4D sensing capabilities
Edge Artificial Intelligence for Real-Time Target Monitoring
The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties.
In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial.
In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device.
Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set
Weimar – a Personal Tribute
Weimar is a relatively small town in the centre of Germany. Around 1552 it became the capital of the small Herzogtum Sachsen-Weimar (Principality Saxony-Weimar), from 1741 until 1918 the capital of the (still relatively small) Principality – since 1815 Grand Principality – (Groß-) Herzogtum Sachsen-Weimar-Eisenach (Saxony-Weimar-Eisenach). After World War I all monarchic structures in Germany were abandoned, the democratic Free State of Thuringia was founded in 1920, and Weimar became its capital until 1950. Despite its moderate size, Weimar managed to gain a cultural profile that extended and still extends far beyond the borders of the (Grand-) Principality, even beyond Germany. The foundations were laid in the 18th and early 19th century, connected to writers and pilosophers like Christoph Martin Wieland, Johann Wolfgang von Goethe, Johann Gottfried von Herder, and Friedrich von Schiller who all lived and worked in Weimar. In the late 19th and early 20th century more writers, musicians and artists contributed to Weimar’s reputation, e.g. Franz Liszt, Richard Strauss, Hugo von Hofmannsthal, Harry Graf Kessler, Henry van de Velde, Edvard Munch, Walter Gropius, Paul Klee, Oskar Schlemmer, Wassily Kandinsky, Lyonel Feininger. In politics, Weimar played ambiguous roles between a comparatively liberal (Grand) Principality, the birth place of the first democratic state in Germany (Weimar Republic), turning “brown” (National-Socialist) from the late 1920s, Communist after World War II, democratic again after the German re-unification in 1990. Weimar is a very special, even intriguing place. This book tries to convey its aura by telling its story from the early beginnings in the 16th century until today, with a main focus on the last three centuries – embedded into pan-German, even pan-European developments
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