Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios

Оптические и спектральные методы в задачах обнаружения и распознавания подвижных летательных объектов

The paper considers a problem of detection and identification of unmanned aerial vehicles (UAVs) against the animate and inanimate objects and identification of their load by optical and spectral optical methods. The state-of-the-art analysis has shown that, when using the radar methods to detect small UAVs, there is a dead zone for distances of 250-700 m, and in this case it is important to use optical methods for detecting UAVs.The application possibilities and improvements of the optical scheme for detecting UAVs at long distances of about 1-2 km are considered. Location is performed by intrinsic infrared (IR) radiation of an object using the IR cameras and thermal imagers, as well as using a laser rangefinder (LIDAR). The paper gives examples of successful dynamic detection and recognition of objects from video images by methods of graph theory and neural networks using the network FasterR-CNN, YOLO and SSD models, including one frame received.The possibility for using the available spectral optical methods to analyze the chemical composition of materials that can be employed for remote identification of UAV coating materials, as well as for detecting trace amounts of matter on its surface has been studied. The advantages and disadvantages of the luminescent spectroscopy with UV illumination, Raman spectroscopy, differential absorption spectroscopy based on a tunable UV laser, spectral imaging methods (hyper / multispectral images), diffuse reflectance laser spectroscopy using infrared tunable quantum cascade lasers (QCL) have been shown.To assess the potential limiting distances for detecting and identifying UAVs, as well as identifying the chemical composition of an object by optical and spectral optical methods, a described experimental setup (a hybrid lidar UAV identification complex) is expected to be useful. The experimental setup structure and its performances are described. Such studies are aimed at development of scientific basics for remote detection, identification, tracking, and determination of UAV parameters and UAV belonging to different groups by optical location and spectroscopy methods, as well as for automatic optical UAV recognition in various environments against the background of moving wildlife. The proposed problem solution is to combine the optical location and spectral analysis methods, methods of the theory of statistics, graphs, deep learning, neural networks and automatic control methods, which is an interdisciplinary fundamental scientific task.Рассматривается проблема обнаружения и идентификации беспилотных летательных аппаратов (БПЛА) на фоне объектов живой и неживой природы, а также идентификации его нагрузки оптическим и спектральными оптическими методами. Анализ современного уровня развития техники  показал, что при использовании радиолокационных методов для обнаружения малых БПЛА для расстояний 250-700 м существует мертвая зона и в этом случае необходимо применение оптических методов обнаружения БПЛА.Рассмотрены возможности и необходимые доработки оптической схемы для обнаружения БПЛА на больших расстояниях порядка 1-2 км по собственному инфракрасному (ИК) излучению объекта с помощью ИК камер и тепловизоров, а также с помощью лазерного дальномера – ЛИДАРА. Приведены примеры успешного динамического обнаружения и распознавания объектов по видео изображениям методами теории графов и нейронных сетей с использованием сетевых моделей FasterR-CNN, YOLO и SSD, в том числе по одному полученному кадру.Проведен анализ возможности использования существующих спектральных оптических методов анализа химического состава материалов, которые могут быть использованы для дистанционной идентификации материалов покрытия БПЛА, а также для обнаружения следовых количеств вещества на его поверхности. Показаны достоинства и недостатки люминесцентной спектроскопии с УФ подсветкой, спектроскопии комбинационного рассеяния света, спектроскопии дифференциального поглощения на основе перестраиваемого УФ лазера, методов формирования спектральных изображений (гипер- / мультиспектральные изображения), лазерной спектроскопию диффузного рассеяния с применением инфракрасных перестраиваемых квантово-каскадных лазеров (ККЛ). Оценку потенциально возможных предельных расстояний обнаружения и идентификации БПЛА, а также идентификации химического состава объекта оптическими и спектральными оптическими методами предполагается провести на создаваемом экспериментальном стенде – гибридном лидарном комплексе идентификации БПЛА. Приводится описание состава экспериментального стенда и его технических характеристик. Целью таких исследований должна стать разработка научных основ дистанционного обнаружения, идентификации, сопровождения и определения параметров БПЛА и принадлежности БПЛА к различным группам методами оптической локации и оптической спектроскопии, а также автоматического оптического распознавания в различных средах на фоне подвижных объектов живой природы. Решение проблемы предлагается вести совмещением методов оптической локации и спектрального анализа, методов теории статистики, графов, машинного обучения, нейронных сетей и методов автоматического управления, что является междисциплинарной фундаментальной научной задачей

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS)

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics