Quantification of the relationship between sea surface roughness and the size of the glistening zone for GNSS-R

A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy (r=0.7

Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios

Exploring space situational awareness using neuromorphic event-based cameras

The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

Pilot sequence based IQ imbalance estimation and compensation

Abstract. As modern radio access technologies strive to achieve progressively higher data rates and to become increasingly more reliable, minimizing the effects of hardware imperfections becomes a priority. One of those imperfections is in-phase quadrature imbalance (IQI), caused by amplitude and phase response differences between the I and Q branches of the IQ demodulation process. IQI has been shown to deteriorate bit error rates, possibly compromise positioning performance, amongst other effects. Minimizing IQI by tightening hardware manufacturing constraints is not always a commercially viable approach, thus, baseband processing for IQI compensation provides an alternative. The thesis begins by presenting a study in IQI modeling for direct conversion receivers, we then derive a model for general imbalances and show that it reproduces the two most common models in the bibliography. We proceed by exploring some of the existing IQI compensation techniques and discussing their underlying assumptions, advantages, and possible relevant issues. A novel pilot-sequence based approach for tackling IQI estimation and compensation is introduced in this thesis. The idea is to minimize the square Frobenius norm of the error between candidate covariance matrices, which are functions of the candidate IQI parameters, and the sample covariance matrices, obtained from measurements. This new method is first presented in a positioning context with flat fading channels, where IQI compensation is used to improve the positioning estimates mean square error. The technique is then adapted to orthogonal frequency division multiplexing (OFDM) systems,including an version that exploits the 5G New Radio reference signals to estimate the IQI coefficients. We further generalize the new approach to solve joint transmitter and receiver IQI estimation and discuss the implementation details and suggested optimization techniques. The introduced methods are evaluated numerically in their corresponding chapters under a set of different conditions, such as varying signal-to-noise ratio, pilot sequence length, channel model, number of subcarriers, etc. Finally, the proposed compensation approach is compared to other well-established methods by evaluating the bit error rate curves of 5G transmissions. We consistently show that the proposed method is capable of outperforming these other methods if the SNR and pilot sequence length values are sufficiently high. In the positioning simulations, the proposed IQI compensation method was able to improve the root mean squared error (RMSE) of the position estimates by approximately 25 cm. In the OFDM scenario, with high SNR and a long pilot sequence, the new method produced estimates with mean squared error (MSE) about a million times smaller than those from a blind estimator. In bit error rate (BER) simulations, the new method was the only compensation technique capable of producing BER curves similar to the curves without IQI in all of the studied scenarios

Assessment of monthly rain fade in the equatorial region at C & KU-band using measat-3 satellite links

C & Ku-band satellite communication links are the most commonly used for equatorial satellite communication links. Severe rainfall rate in equatorial regions can cause a large rain attenuation in real compared to the prediction. ITU-R P. 618 standards are commonly used to predict satellite rain fade in designing satellite communication network. However, the prediction of ITU-R is still found to be inaccurate hence hinder a reliable operational satellite communication link in equatorial region. This paper aims to provide an accurate insight by assessment of the monthly C & Ku-band rain fade performance by collecting data from commercial earth stations using C band and Ku-band antenna with 11 m and 13 m diameter respectively. The antennas measure the C & Ku-band beacon signal from MEASAT-3 under equatorial rain conditions. The data is collected for one year in 2015. The monthly cumulative distribution function is developed based on the 1-year data. RMSE analysis is made by comparing the monthly measured data of C-band and Ku-band to the ITU-R predictions developed based on ITU-R’s P.618, P.837, P.838 and P.839 standards. The findings show that Ku-band produces an average of 25 RMSE value while the C-band rain attenuation produces an average of 2 RMSE value. Therefore, the ITU-R model still under predicts the rain attenuation in the equatorial region and this call for revisit of the fundamental quantity in determining the rain fade for rain attenuation to be re-evaluated

Territorial governance and climate adaptation. Towards an environmental perspective of urban regeneration

The literature, the scientific and disciplinary debate and the growing awareness on the part of national and international bodies of the impact of climate change on the territory (European Green Deal, 2019, Horizon Europe 2021-2027) have highlighted, in recent years, the need for climate-proof policies, strategies, tools and actions as a goal to be achieved through the updating and innovation of spatial government tools, according to an integrated and inter-scalar approach for the construction of urban regeneration strategies in coherence with the objectives of the European Strategy on adaptation to climate change (EU, 2021) and the addresses of the 17 Sustainable Development Goals of the 2030 Agenda for Sustainable Development (UN, 2015) and the Climate Conference (COP26, 2021). This scenario is reflected in the most recent EU programming and policies and is, moreover, a transversal objective of the PNRR (Mission 5 Inclusion and Cohesion (Urban Regeneration and Social Housing), as well as of the PNR 2021/2027 (AT 2 Humanistic Culture, Creativity, Social Transformation, Inclusive Society in close correlation with AT 5 Climate and AT 6 Environment). In this context, the contribution presents some of the results of the research activities carried out by the authors that highlight, starting from the analysis of national and international planning experiences and best practices, the urgency of defining new perspectives and new theoretical-methodological and operational references for an innovative planning system, as a tool for a sustainable and resilient regeneration of contemporary cities and territories, at the supra-municipal, municipal and local scale, with significant impacts on mitigation and adaptation to the effects of climate change

Characterising Spatial and Temporal Ionospheric Variability with a Network of Oblique Angle-of-arrival and Doppler Ionosondes

Ionospheric variability exists on a broad range of scales, and routinely impacts skywave propagation modes of high frequency radio waves, to the detriment of radar and communication systems. In order to better understand the electron density structures associated with such variability at mid-latitudes, a network of oblique angle-of-arrival (AoA) and Doppler ionosondes were installed in central and northern Australia as part of the ELOISE campaign in 2015. This thesis analyses observations from the ELOISE AoA ionosondes, with a focus on characterising the influence of medium- to large- scale gradients and signatures of travelling ionospheric disturbances (TIDs). Following an overview of the experiment, the design and calibration of the new ionosonde system is described. With multi-channel receivers connected to each element of two twin-arm arrays, a total of eleven AoA paths of between 900 and 2700 km were collected, including nine with interleaved Doppler measurements using a special channel scattering function (CSF) capability. On-board signal processing was developed to perform real-time clear channel evaluation and CSF scheduling, and generate the AoA ionograms and delay-Doppler images with fitted electron density profiles. In further offline analysis, peak detection and mode classification was carried out, to support reflection point mapping and tilt estimation. Significant testing and validation of the new ionosonde before and after the experiment revealed AoA uncertainties on the scale of 0.2–0.5° in bearing and 0.4–0.9° in elevation. Having identified a low-elevation bias, models of tropospheric refraction and antenna mutual coupling effects were considered as possible correction strategies, but ultimately an empirical approach based on aggregated ionospheric returns was implemented. Small-scale (intra-dwell) ionospheric variability also has the potential to compromise results, through unresolved multi-mode mixing, and this has been investigated using a combination of spatial and temporal variability metrics derived from the CSF data. The analysis of large quantities of F2 peak data shows persistent diurnal patterns in the oblique AoA observables that are also well-captured by a conventional data-assimilative ionospheric model, even without the benefit of AoA and Doppler inputs. Furthermore, Doppler measurements are reproduced remarkably well using just the midpoint fitted profiles. A statistical study has quantified the level of consistency between observations and model, to provide greater confidence in the results. Many of the geophysical features can be interpreted as ionospheric gradients, as evident in the tilt estimates, and horizontally moving structures such as TIDs, using a form of Doppler-based drift analysis. While signatures of TIDs vary considerably, two simple wave-like perturbation models have been evaluated to help classify quasi-periodic behaviour in the AoA observations, as well as understand the directional filtering effect imposed by the path geometry. In some cases, a set of TID parameters can be determined by eye, but in others automatic parameter inversion techniques may be more viable. Two such techniques were implemented but results using both real and synthetic data demonstrated some significant limitations. Finally, attempts to relate TID signatures across multiple paths shows promise, but there still appears to be a strong dependence on path geometry that is difficult to eliminate.Thesis (Ph.D.) -- University of Adelaide, School of Physical Sciences, 202

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control