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This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic method, producing a distributed and…
The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become…
The increasing deployment of small drones as tools of conflict and disruption has amplified their threat, highlighting the urgent need for effective anti-drone measures. However, the compact size of most drones presents a significant…
The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a…
This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios…
Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned…
Measured impulse responses obtained from a dynamic unmanned aerial vehicle (UAV) channel sounding system exhibit effects attributable to time-varying carrier frequency offset (CFO) and sampling frequency offset (SFO). To correct the…
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision…
Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we…
Large-scale environmental sensing with a finite number of mobile sensors is a challenging task that requires a lot of resources and time. This is especially true when features in the environment are spatiotemporally changing with unknown or…
Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks…
In emergency scenarios, unmanned aerial vehicles (UAVs) can be deployed to assist localization and communication services for ground terminals. In this paper, we propose a new integrated air-ground networking paradigm that uses…
Spatial fields in the Earth and environmental sciences are often available at multiple scales or resolutions. While coarse-scale data (e.g., from global circulation models) are often abundant, they lack the local detail provided by…
Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management. Despite the remarkable progress of deep…
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the…
In this paper, we establish the almost sure convergence of two-timescale stochastic gradient descent algorithms in continuous time under general noise and stability conditions, extending well known results in discrete time. We analyse…
Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and…
Depth completion and object detection are two crucial tasks often used for aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial Vehicles (UAVs). Common solutions include using measurements from a LiDAR sensor;…
Semantic segmentation from RGB cameras is essential to the perception of autonomous flying vehicles. The stability of predictions through the captured videos is paramount to their reliability and, by extension, to the trustworthiness of the…
Advancements in analog-to-digital converter (ADC) technology have enabled higher sampling rates, making it feasible to adopt digital radar architectures that directly sample the radio-frequency (RF) signal, eliminating the need for analog…