This lecture provides a timely update on Unmanned Aircraft Systems (UAS) research and innovation initiatives, with a focus on avionics and Air Traffic Management (ATM) evolutions. Fundamental topics covered encompass onboard Communication, Navigation and Surveillance (CNS) systems, and their interactions within current and future ATM frameworks. The lecture also delves into the relevant cybersecurity principles within the UAS Traffic Management (UTM) and Urban Air Mobility (UAM) domains, aiming to identify the possible threats and applicable mitigation strategies. Beginning with an introductory segment outlining the IEEE AESS Avionics Systems Panel (ASP) activities in this domain, the lecture provides a high-level classification of Cyber-Physical Aerospace Systems (CPAS) and discusses the UAS as a system-of-systems, with associated key definitions. Subsequent sections delve into ongoing research and innovation efforts, including: autonomous navigation and guidance, sense-and-avoid, human-machine interactions, AI integration in UAS avionics design, cybersecurity, and ongoing UTM/UAM airspace integration initiatives. This lecture is intended for researchers, students and professionals seeking to gain insights into the rapidly evolving domain of UAS and associated airspace integration efforts.

There has been an increased interest in the Moon recently, not just for its own exploration, but as a gateway for searching other planets as well as the deep space. To facilitate future exploration endeavors on Moon, Lunar Communications Relay and Navigation Systems (LCRNS) are proposed that will enable Position, Navigation, and Timing (PNT), along with other functionalities. This paper focuses on the Lunar Navigation Services (LNS), achieving full coverage using a lunar relay constellation. Five satellite constellations consisting of four satellites in Elliptical Lunar Frozen (ELF) orbits and one in a circular orbit around the equator are assessed for their availability. Assuming the integrity criteria having been met, satellite-receiver relative geometry can provide good indication of LNS availability. Also, for accurate PNT on the lunar surface, a lunar time must be devised, considering the limited capability for Earth-based ground stations. The LNS was analyzed for availability on the lunar surface, especially focusing on the South Pole region. A fully operational LCRNS will enable complex surface and near-surface operations, including Search and Rescue (SAR), situational awareness, and safe lunar descent enabling soft-landing, especially in the undulating lunar terrain.

Resident Space Objects (RSO) include satellites, spacecrafts, and other equipment remaining in Earth's orbit for an extended period following activities such as space launches, orbital missions and collisions, thereby posing a formidable threat to space infrastructure and operations due to the large uncertainty of their population, trajectories, mass, size, etc. It is therefore necessary not only to track the total number of objects in space, but also to continuously estimate the trajectory of these objects and probability of accidental collisions with other objects. At present, RSO are tracked and catalogued using ground-based observations, but Space-Based Space Surveillance (SBSS) represents a valid complementary alternative due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. Accurate and continuous orbit determination of RSO is essential for establishing a unique scheme for an accurate prediction of the RSO dynamics for applications like Point-To-Point Suborbital Transport (PPST) that are envisioned to be commercialized in future. This article proposes an innovative trajectory estimation algorithm through Data Fusion from multiple Electro-Optical (EO) sensors performing Space-Based Space Surveillance (SBSS). A verification case study is performed on a constellation of Distributed Satellite Systems (DSS) that aim to carry out a piggy-backed mission by performing SBSS and Earth observation operations simultaneously.

Urban Air Mobility (UAM) aims to establish a low-altitude transportation system that operates safely and efficiently to mitigate the increasing ground traffic congestion in densely populated areas. Various aircraft types, including Passenger Aerial Vehicles (PAV) and Unmanned Aerial Vehicles (UAV), will be used to provide UAM services. In this context, a large number of aircraft are expected to operate in close proximity to each other, leading to challenges in terms of communication throughput and interference. To address these challenges, this paper examines UAM communication requirements and the potential applications of cellular networks in the relevant flight environments. UAM wireless connectivity performance is analyzed focusing on co-channel interference and mathematical expressions for the Probability of Coverage (PoC) are derived using stochastic geometry. Based on these premises, the improvements in PoC attainable using interference mitigation techniques such as Frequency Reuse (FR) and Separation Distance (SD) are investigated. Then, a PoC enhancement algorithm is presented using a combined FR-SD method. Numerical verification case studies are performed in representative conditions, showing that the proposed method is able to mitigate co-channel interference, significantly reducing computational time and increasing spectrum efficiency.

This presentation discusses the conceptual design, prototyping and verification of Cognitive Human-Machine Interfaces and Interaction (CHMI2) systems to provide adaptive automation in next-generation avionics and Air Traffic Management (ATM) systems, with a focus on current low-altitude airspace evolutions for UAS Traffic Management (UTM) and Urban Air Mobility (UAM). The adaptive automation capability offered by the CHMI2 system can provide a pathway towards higher levels of human-machine teaming to support trusted autonomous operations. Three potential applications are considered: (1) Virtual Pilot Assistant (VPA) system for commercial Single-Pilot operated aircraft; (2) Ground Control Station (GCS) supporting Multi-Unmanned Aircraft Systems (UAS) operations; (3) Dynamic Airspace Management of UTM and UAM. The core CHMI2 system comprises three modules, namely: sensing, estimation and adaptation. The sensing module consists of a suite of sensors and algorithms for observing and extracting suitable physiological features of the user. The estimation module contains models that translate the features from the sensing module into measures of the user’s cognitive state. The adaptation module contains the logics that drive adaptation in the Human-Machine Interface (HMI) and system automation modes based on the estimated cognitive states. Development and test activities are currently ongoing, focused on verifying the performance of each individual module in the intended operational environment and include Human-in-the-Loop (HITL) testing of the prototype systems.