In-Soil Measuring of Sugar Beet Yield Using UWB Radar Sensor System

Yield mapping is a basic entity of the Precision Farming concept and provides crucial information about the success of cultivation. Several approaches to site-specific yield recording during the sugar beet harvest are known. Most of them are based on the weighing of sugar beets together with soil tare. Another real-time yield mapping approach with the option of plant population counting is based on estimating the mass of individual sugar beets on the basis of their maximal diameter. The main goal of the research was to develop and evaluate a yield recording procedure based on radar technology, which will provide non-invasive in-soil detection and identification of single sugar beets in order to enable the counting of individual sugar beets and determining of the single sugar beet root mass. Further goals were to enhance the radar technology for other applications in the agriculture, as a general goal, and to define applicability restrictions of practical utilisation of the system for the sugar beet and similar crops. The research activities have been divided into laboratory and field experiments. The results of the laboratory experiments have provided valuable information about the measuring system’s behaviour, which enabled the successful field measurements. The used method allowed the identification and detection of 90% to 96% of sugar beets under test in the various field conditions, with correlation coefficients between real sugar beet positions and detected positions of about 99%, and average positioning error from 1,1 to 3,6 cm. The correlation coefficients between single sugar beet root masses and recorded reflected energy amounts were for the majority of tests over 70%, and the best results have been on the level close to 90%. This project was a joint venture of the Institute for Agricultural Engineering from Bonn and the Technical University of Ilmenau.Teilflächenspezifische Ertragsmessung von Zuckerrüben im Boden mittels UWB Radarsensorsystem Die Ertragskartierung ist ein wesentlicher Bestandteil des Konzeptes „Precision Farming“. Die Erntemasse von Kulturpflanzen ist für den Landwirt eine elementare Information über den Erfolg pflanzbaulicher Maßnahmen. Es sind mehrere Verfahren zur Ertragsermittlung von Zuckerrüben während der Ernte mit dem Bezug auf Teilflächen bekannt. Ein sensorischer Ansatz besteht in der Pflanzenzählung und Ermittlung der Masse der einzelnen Zuckerrüben über den maximalen Durchmesser. Das Hauptziel dieser Forschungsarbeiten war die Entwicklung und Bewertung eines berührungslosen Ertragserfassungssystems für Zuckerrüben, das teilflächenbasiert eine Zählung und Massebestimmung der Einzelrüben ermöglicht. Die weiteren Ziele bestanden in der Weiterentwicklung der Radartechnologie für andere Einsatzgebiete der Landwirtschaft und in der Bestimmung der Anwendbarkeitsgrenzen des Systems für Zuckerrüben und ähnliche Wurzelfrüchte. Die Forschungsaktivitäten fanden im Labor und unter Feldbedingungen auf Versuchsparzellen eines typischen Zuckerrübenstandortes statt. Die Ergebnisse unter Laborbedingungen lieferten wertvolle Informationen, die erfolgreiche Feldmessungen ermöglicht haben. Die angewendete Methode hat in unterschiedlichen Messbedingungen eine 90% bis 96% erfolgreiche Zuckerrübenidentifikation ermöglicht, mit Korrelationskoeffizienten zwischen tatsächlichen und detektierten Zuckerrübenpositionen von um 99% und einem durchschnittlichen Positionierungsfehler von 1,1 bis 3,6 cm. Die Korrelationskoeffizienten zwischen der Einzelrübenmasse und der gemessenen reflektierten Energiemenge lagen im Bereich von über 70% und die besten Ergebnisse erreichten Werte von 90%. Das Projekt wurde in der Zusammenarbeit des Instituts für Landtechnik Bonn und des Instituts für Kommunikations- und Messtechnik der Technischen Universität Ilmenau durchgeführt

Development and Evaluation of a Multistatic Ultrawideband Random Noise Radar

This research studies the AFIT noise network (NoNET) radar node design and the feasibility in processing the bistatic channel information of a cluster of widely distributed noise radar nodes. A system characterization is used to predict theoretical localization performance metrics. Design and integration of a distributed and central signal and data processing architecture enables the Matlab®-driven signal data acquisition, digital processing and multi-sensor image fusion. Experimental evaluation of the monostatic localization performance reveals its range measurement error standard deviation is 4.8 cm with a range resolution of 87.2(±5.9) cm. The 16-channel multistatic solution results in a 2-dimensional localization error of 7.7(±3.1) cm and a comparative analysis is performed against the netted monostatic solution. Results show that active sensing with a low probability of intercept (LPI) multistatic radar, like the NoNET, is capable of producing sub-meter accuracy and near meter-resolution imagery

Multi-Sensor Data Fusion between Radio Tomographic Imaging and Noise Radar

The lack of situational awareness within an operational environment is a problem that carries high risk and expensive consequences. Radio Tomographic Imaging (RTI) and noise radar are two proven technologies capable of through-wall imaging and foliage penetration. The intent of this thesis is to provide a proof of concept for the fusion of data from RTI and noise radar. The output of this thesis will consist of a performance comparison between the two technologies followed by the derivation of a fusion technique to produce a single image. Proposals have been made for the integration of multiple-input multiple-output (MIMO) radar with RTI, however, no research has been done. Data fusion between RTI and noise radar has not been explored in academia. The impact of the expected results will provide the RTI and noise radar community a proof of concept for the fusion of data from two disparate sensor technologies. RTI is a tenured field of study at Air Force Institute of Technology (AFIT), whose results can be used to produce a platform for further options to be considered for military surveillance applications. The novelty of fusing data from RTI and noise radar is achieved with the derivation of a fusion technique utilizing Tikhonov regularization. Analyzing the results of the Tikhonov influenced techniques reveals up to a 100% error decrease in target pixel location, a 75% error decrease in target centroid location, a 28% size decrease in target pixel dispersion and a 72% improvement in an ideal solution comparison. The results of the research prove that Multi-Sensor Data Fusion (MSDF) images are of greater quality than that of the images generated by the disparate sensors independently. This effectively provides the RTI and noise radar communities a proof of concept for the fusion of data from two disparate sensor technologies

Analysis of Ultra Wide Band (UWB) Technology for an Indoor Geolocation and Physiological Monitoring System

The goal of this research is to analyze the utility of UWB for indoor geolocation and to evaluate a prototype system, which will send information detailing a person’s position and physiological status to a command center. In a real world environment, geolocation and physiological status information needs to be sent to a command and control center that may be located several miles away from the operational environment. This research analyzes and characterizes the UWB signal in the various operational environments associated with indoor geolocation. Additionally, typical usage scenarios for the interaction between UWB and other devices are also tested and evaluated

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Doctor of Philosophy

dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Mobile Radio Channel Measurements for air-to-ground and non-conventional future applications

La tesi si suddivide in quattro parti: due iniziali di tipo compilativo e le altre due sperimentali. Nella prima parte vengono descritti gli UAVs: classificazioni e applicazioni da un punto di vista delle telecomunicazioni e della sicurezza; una seconda parte sempre compilativa, espone invece una panoramica sulle caratteristiche del canale Air-to-Ground e la possibilità di modelling attraverso diversi scenari. La terza parte rappresenta il corpo della tesi, in quanto presenta la descrizione di una campagna di misure condotta in ambiente industriale, fatta con due diversi setup di misure: onde mm e UWB. Dopo la presentazione dello scopo, vengono poi trattati gli esperimenti, descritto l'equipment ed estratte le conclusioni mostrando funzioni come il Power Angle Profile e la Risposta Impulsiva. L'ultimo capitolo tratta infine di una campagna da condurre in ambiente urbano, presentando però solo il piano di misure, in quanto i risultati saranno a breve disponibili