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

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Wi-Fi Enabled Healthcare

Focusing on its recent proliferation in hospital systems, Wi-Fi Enabled Healthcare explains how Wi-Fi is transforming clinical work flows and infusing new life into the types of mobile devices being implemented in hospitals. Drawing on first-hand experiences from one of the largest healthcare systems in the United States, it covers the key areas associated with wireless network design, security, and support. Reporting on cutting-edge developments and emerging standards in Wi-Fi technologies, the book explores security implications for each device type. It covers real-time location services and emerging trends in cloud-based wireless architecture. It also outlines several options and design consideration for employee wireless coverage, voice over wireless (including smart phones), mobile medical devices, and wireless guest services. This book presents authoritative insight into the challenges that exist in adding Wi-Fi within a healthcare setting. It explores several solutions in each space along with design considerations and pros and cons. It also supplies an in-depth look at voice over wireless, mobile medical devices, and wireless guest services. The authors provide readers with the technical knowhow required to ensure their systems provide the reliable, end-to-end communications necessary to surmount today’s challenges and capitalize on new opportunities. The shared experience and lessons learned provide essential guidance for large and small healthcare organizations in the United States and around the world. This book is an ideal reference for network design engineers and high-level hospital executives that are thinking about adding or improving upon Wi-Fi in their hospitals or hospital systems

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Five decades of radioglaciology

Radar sounding is a powerful geophysical approach for characterizing the subsurface conditions of terrestrial and planetary ice masses at local to global scales. As a result, a wide array of orbital, airborne, ground-based, and in situ instruments, platforms and data analysis approaches for radioglaciology have been developed, applied or proposed. Terrestrially, airborne radar sounding has been used in glaciology to observe ice thickness, basal topography and englacial layers for five decades. More recently, radar sounding data have also been exploited to estimate the extent and configuration of subglacial water, the geometry of subglacial bedforms and the subglacial and englacial thermal states of ice sheets. Planetary radar sounders have observed, or are planned to observe, the subsurfaces and near-surfaces of Mars, Earth's Moon, comets and the icy moons of Jupiter. In this review paper, and the thematic issue of the Annals of Glaciology on ‘Five decades of radioglaciology’ to which it belongs, we present recent advances in the fields of radar systems, missions, signal processing, data analysis, modeling and scientific interpretation. Our review presents progress in these fields since the last radio-glaciological Annals of Glaciology issue of 2014, the context of their history and future prospects

Underwater mapping using a SONAR

Este estudo explora a capacidade física do raio de um sonar mecânico para construir um mapa do ambiente envolvente e encontrar a localização do veículo nesse mesmo mapa. Os dados do sonar alimentam um extrator de pontos de interesse e um algoritmo SLAM. Esse algoritmo é composto por uma implementação de Octomaps, junto com um filtro de partículas. Vários testes foram executados dentro de um ambiente estruturado e os resultados desses testes são demonstrados neste estudo.This study explores the physical capabilities of the beam of a mechanical scanning imaging sonar to build a map of the surrounding environment and to find the location of the vehicle within the map. The data from the sonar feed into a feature extractor and a SLAM algorithm. The SLAM algorithm is composed of an Octomaps implementation together with a particle filter. Several tests were ran within a structured environment and the map of the structured environment as well as the location of the vehicle are presented

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet