New target tracking and monitoring guidance laws for UAV

A pursuer UAV tracking and loitering around a target is the problem analyzed in this thesis. The UAV is assumed to be a fixed-wing vehicle and constant airspeed together with bounded lateral accelerations are the main constraints of the problem. Three different guidance laws are designed for ensuring a continuos overfly on the target. Different proofs are presented to demonstrate the stability properties of the laws. All the algorithms are tested on a 6DoF Pioneer software simulator. Classic control design methods have been adopted to develop autopilots for implementig the simulation platform used for testing the guidance laws.

Code synchronization and interference management techniques for satellite navigation and communications

This thesis collects the outcomes of a Ph.D. course in Telecommunications engineering and it is focused on enabling techniques for Spread Spectrum (SS) navigation and communication satellite systems. It provides innovations for both interference management and code synchronization techniques. These two aspects are critical for modern navigation and communication systems and constitute the common denominator of the work. The thesis is organized in two parts: the former deals with interference management. We have proposed a novel technique for the enhancement of the sensitivity level of an advanced interference detection and localization system operating in the Global Navigation Satellite System (GNSS) bands, which allows the identification of interfering signals received with power even lower than the GNSS signals. Moreover, we have introduced an effective cancellation technique for signals transmitted by jammers, exploiting their repetitive characteristics, which strongly reduces the interference level at the receiver. The second part, deals with code synchronization. More in detail, we have designed the code synchronization circuit for a Telemetry, Tracking and Control system operating during the Launch and Early Orbit Phase; the proposed solution allows to cope with the very large frequency uncertainty and dynamics characterizing this scenario, and performs the estimation of the code epoch, of the carrier frequency and of the carrier frequency variation rate. Furthermore, considering a generic pair of circuits performing code acquisition, we have proposed a comprehensive framework for the design and the analysis of the optimal cooperation procedure, which minimizes the time required to accomplish synchronization. The study results particularly interesting since it enables the reduction of the code acquisition time without increasing the computational complexity. Finally, considering a network of collaborating navigation receivers, we have proposed an innovative cooperative code acquisition scheme, which allows exploit the shared code epoch information between neighbor nodes, according to the Peer-to-Peer paradigm.

Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.

Model predictive flight control systems for rotorcraft UAS

Constraints are widely present in the flight control problems: actuators saturations or flight envelope limitations are only some examples of that. The ability of Model Predictive Control (MPC) of dealing with the constraints joined with the increased computational power of modern calculators makes this approach attractive also for fast dynamics systems such as agile air vehicles. This PhD thesis presents the results, achieved at the Aerospace Engineering Department of the University of Bologna in collaboration with the Dutch National Aerospace Laboratories (NLR), concerning the development of a model predictive control system for small scale rotorcraft UAS. Several different predictive architectures have been evaluated and tested by means of simulation, as a result of this analysis the most promising one has been used to implement three different control systems: a Stability and Control Augmentation System, a trajectory tracking and a path following system. The systems have been compared with a corresponding baseline controller and showed several advantages in terms of performance, stability and robustness.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Smart tracking of composite parts: feasibility study and effect on mechanical behaviour

This research proposes a solution for integrating RFID - Radio Frequency Identification technology within a structure based on CFRPs - Carbon Fiber Reinforced Polymers. Therefore, the main objective is to use technology to monitor and track composite components during manufacturing and service life. The study can be divided into two macro-areas. The first portion of the research evaluates the impact of the composite materials used on transmitting the electromagnetic signal to and from the tag. RFID technology communicates through radio frequencies to to track and trace items associated with the tags. In the first instance, a feasibility study was carried out to assess using commercially available tags. Then, after evaluating different solutions, it was decided to incorporate the tags into coupons during production. The second portion of the research is focused on evaluating the impact on the composite material's resistance to tag embedding. It starts with designing tensile test specimens through the FEM model with different housing configurations. Subsequently, the best configuration was tested in the facilities of the In the Faculty of Aerospace Engineering at TU Delft, particularly in the Structure & Materials Laboratory, two tests were conducted: the first one based on ASTM D3039/D3039 - 14 - Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials, the second one dividing the path to failure into failure intervals in a load-unload-reload. Both tests were accompanied by instruments such as DIC, AE, C-Scan and Optical Microscopes. The expected result of the inclusion of RFID tags in composite components is that it brings added value to the parts with which it is associated without affecting too much its mechanical properties. This comes first from the automatic identification of RFID during the production cycle and its useful life. As a result, improvements were made in the design of production facilities.

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.