The increasing use of Unmanned Aerial Vehicles (UAVs) in safety-critical missions in both civilian and military areas demands accurate and reliable navigation, where one of the key sources of navigation information is presented by Global Navigation Satellite Systems (GNSS). In challenging conditions, for example, in urban areas, the accuracy of GNSS-based navigation may degrade significantly due to user-satellite geometry and obscuration issues without being noticed by the user. Therefore, considering the essentially dynamic rate of change in this type of environment, integrity monitoring is of critical importance for understanding the level of trust we have in positioning and timing data. In this paper, the dilution of precision (DOP) coefficients under nominal and challenging conditions were investigated for the purpose of integrity monitoring in urban environments. By analyzing positioning information in a simulated urban environment using a software-based GNSS receiver, the integrity monitoring approach based on joint consideration of GNSS observables and environmental parameters has been proposed. It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches. This has allowed generating indirect integrity measures based on cut-off elevation and satellite visibility that can be used for UAV path planning and guidance in urban environments.

An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches. The aim of the proposed system is that an ally UAV can either avoid or track an unexpected enemy UAV with a net to protect itself. In this point of view, a simple and robust target detection, tracking and avoidance system is designed. Two open-source tools were used for the aim: a state-of-the-art object detection technique called SSD and an API for MAVLink compatible systems called MAVSDK. The MAVSDK performs velocity control when a UAV is detected so that the manoeuvre is done simply and efficiently. The proposed system was verified with Software in the loop (SITL) and Hardware in the loop (HITL) simulators. The simplicity of this algorithm makes it innovative, and therefore it should be used in future applications needing robust performances with low-cost hardware such as delivery drone applications.

This paper proposes an autopilot design for an interceptor with Thrust-Vector-Control (TVC) that operates in the endo- and exo-atmospheric regions. The main objective of the proposed autopilot design is to ensure control performance in both atmospheric regions, without changing the control mechanism. In this paper, the characteristics of the aerodynamic forces in both atmospheric regions are first investigated to examine the issue of the conventional control mechanism at various altitudes. And then, a control mechanism, which can be applied to both atmospheric regions, is determined based on the analysis results. An autopilot design is then followed by utilizing the control mechanism and the feedback linearization control (FBLC) method. Accordingly, the proposed autopilot does not rely on changing the control mechanism depending on flight condition unlike the conventional approach as well as it can adjust the control gains automatically according to the changes of flight operating conditions. In this paper, the robustness of the proposed autopilot is investigated through the tracking error analysis and the relative stability analysis in the presence of model uncertainties. The physical meaning of the proposed autopilot is also presented by comparing to the well-known three-loop control structure. Finally, numerical simulations are performed to show the performance of the proposed method.