Automotive Radars:towards smarter vehicles

Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.

Analisi sperimentale di un sistema di localizzazione UWB per applicazioni robotiche

Data la pervasività dell’elettronica nella vita moderna, sempre più interesse è stato posto nelle applicazioni di localizzazione. In ambienti indoor e outdoor, dove sono richieste precisioni dell’ordine delle decine di centimetri, i classici servizi GNSS non sono applicabili. Una tecnologia promettente è quella a banda ultra-larga (UWB), affermata recentemente sul mercato. Un’alternativa potrebbe essere l’utilizzo di sensori ottici con marker di riferimento, però la presenza di fumo e idrometeore presenti nell’ambiente potrebbero andare ad oscurare i marker, rendendo impossibile la localizzazione. Lo scopo della tesi è volto a studiare un sistema di localizzazione UWB, progettato dall’Università di Bologna, ora sviluppato e commercializzato, in un’applicazione robotica. In particolare, l’idea è quella di porre il sistema di localizzazione sopra un elemento mobile, per esempio nella parte terminale di un braccio meccanico, al fine di localizzare un oggetto dotato di nodo UWB, ricavando dinamicamente la posizione relativa tra il braccio e l’oggetto. La tesi si concentra inizialmente a livello teorico sulle caratteristiche delle principali tecnologie coinvolte, in particolare i sistemi UWB e il filtraggio Bayesiano, per poi trattare i limiti teorici della localizzazione, nei concetti di Cramér-Rao lower bound (CRLB) e geometric dilution of precision (GDOP). Da questi, si motiva l’utilizzo della tecnica two-way ranging (TWR) come principio di raccolta delle misure per la localizzazione, per poi ricavare le curve teoriche dell’errore di localizzazione, tenendo conto dei limiti geometrici imposti, utilizzate come riferimento per l’analisi dell’errore ottenuto con il sistema reale. La tecnologia viene testata in tre campagne di misura. Nelle prime due si osserva la presenza di un piano metallico parallelo alla direzione di propagazione, per esaminare gli effetti delle riflessioni sulla localizzazione, mentre nella terza campagna si considera la presenza di acqua dolce.

Deep and near space tracking stations in support of lunar and planetary exploration missions

The aim of this dissertation is to describe the methodologies required to design, operate, and validate the performance of ground stations dedicated to near and deep space tracking, as well as the models developed to process the signals acquired, from raw data to the output parameters of the orbit determination of spacecraft. This work is framed in the context of lunar and planetary exploration missions by addressing the challenges in receiving and processing radiometric data for radio science investigations and navigation purposes. These challenges include the designing of an appropriate back-end to read, convert and store the antenna voltages, the definition of appropriate methodologies for pre-processing, calibration, and estimation of radiometric data for the extraction of information on the spacecraft state, and the definition and integration of accurate models of the spacecraft dynamics to evaluate the goodness of the recorded signals. Additionally, the experimental design of acquisition strategies to perform direct comparison between ground stations is described and discussed. In particular, the evaluation of the differential performance between stations requires the designing of a dedicated tracking campaign to maximize the overlap of the recorded datasets at the receivers, making it possible to correlate the received signals and isolate the contribution of the ground segment to the noise in the single link. Finally, in support of the methodologies and models presented, results from the validation and design work performed on the Deep Space Network (DSN) affiliated nodes DSS-69 and DSS-17 will also be reported.

Radiation characteristics enhancement of planar structures using metasurfaces

In this thesis, the focus is on utilizing metasurfaces to improve radiation characteristics of planar structures. The study encompasses various aspects of metasurface applications, including enhancing antenna radiation characteristics and manipulating electromagnetic (EM) waves, such as polarization conversion and anomalous reflection. The thesis introduces the design of a single-port antenna with dual-mode operation, integrating metasurfaces. This antenna serves as the front-end for a next-generation tag, functioning as a position sensor with identification and energy harvesting capabilities. It operates in the lower European Ultra-Wideband (UWB) frequency range for communication/localization and the UHF band for wireless energy reception. The design aims for a low-profile stack-up that remains unaffected by background materials. Researchers worldwide are drawn to metasurfaces due to their EM wave manipulation capabilities. The thesis also demonstrates how a High-Impedance Surface (HIS) can enhance the antenna's versatility through metasurface application, including conformal design using 3D-printing technology, ensuring adaptability for various deformation and tracking/powering scenarios. Additionally, the thesis explores two distinct metasurface applications. One involves designing an angularly stable super-wideband Circular Polarization Converter (CPC) operating from 11 to 35GHz with an impressive relative impedance bandwidth of 104.3%. The CPC shows a stable response even at oblique incidences up to 40 degrees, with a Peak Cross-Polarization Ratio (PCR) exceeding 62% across the entire band. The second application focuses on an Intelligent Reflective Surface (IRS) capable of redirecting incoming waves in unconventional directions. Tunability is achieved through an artificially developed ferroelectric material (HfZrO) and distributed capacitive elements (IDC) to fine-tune impedance and phase responses at the meta-atom level. The IRS demonstrates anomalous reflection for normal incident waves. These innovative applications of metasurfaces offer promising advancements in antenna design, EM wave manipulation, and versatile wireless communication systems.

Performance Analysis of 5G based Positioning in Railway Applications

Rail transportation has significant importance in the future world. This importance is tightly bounded to accessible, sustainable, efficient and safe railway systems. Precise positioning in railway applications is essential for increasing railway traffic, train-track control, collision avoidance, train management and autonomous train driving. Hence, precise train positioning is a safety-critical application. Nowadays, positioning in railway applications highly depends on a cellular-based system called GSM-R, a railway-specific version of Global System for Mobile Communications (GSM). However, GSM-R is a relatively outdated technology and does not provide enough capacity and precision demanded by future railway networks. One option for positioning is mounting Global Navigation Satellite System (GNSS) receivers on trains as a low-cost solution. Nevertheless, GNSS can not provide continuous service due to signal interruption by harsh environments, tunnels etc. Another option is exploiting cellular-based positioning methods. The most recent cellular technology, 5G, provides high network capacity, low latency, high accuracy and high availability suitable for train positioning. In this thesis, an approach to 5G-based positioning for railway systems is discussed and simulated. Observed Time Difference of Arrival (OTDOA) method and 5G Positioning Reference Signal (PRS) are used. Simulations run using MATLAB, based on existing code developed for 5G positioning by extending it for Non Line of Sight (NLOS) link detection and base station exclusion algorithms. Performance analysis for different configurations is completed. Results show that efficient NLOS detection improves positioning accuracy and implementing a base station exclusion algorithm helps for further increase.