Related papers: Radar and Acoustic Sensor Fusion using a Transform…
As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly…
The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday…
Radio Frequency (RF) fingerprinting offers a promising approach for drone identification and security, although it suffers from significant performance degradation when operating on different transmission channels. This paper presents…
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly…
Detecting small drones, often indistinguishable from birds, is crucial for modern surveillance. This work introduces a drone detection methodology built upon the medium-sized YOLOv11 object detection model. To enhance its performance on…
Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object…
Multimodal sensor fusion is an essential capability for autonomous robots, enabling object detection and decision-making in the presence of failing or uncertain inputs. While recent fusion methods excel in normal environmental conditions,…
Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape…
Robust long-term tracking of drone is a critical requirement for modern surveillance systems, given their increasing threat potential. While detector-based approaches typically achieve strong frame-level accuracy, they often suffer from…
Unmanned Aerial Vehicles (UAVs), popularly called drones, are an important part of future wireless communications, either as user equipment that needs communication with a ground station, or as base station in a 3D network. For both the…
The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably detect these targets due to their small size, high agility,…
Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accuracy and robustness in various environments. Millimeter-wave…
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more…
The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that…
Radar is ubiquitous in autonomous driving systems due to its low cost and good adaptability to bad weather. Nevertheless, the radar detection performance is usually inferior because its point cloud is sparse and not accurate due to the poor…
Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radars or cameras, raise deployment costs and burden lightweight drones…
In recent years, the illicit use of unmanned aerial vehicles (UAVs) for deliveries in restricted area such as prisons became a significant security challenge. While numerous studies have focused on UAV detection or localization, little…
Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly…
To accurately estimate locations and velocities of surrounding targets (cars) is crucial for advanced driver assistance systems based on radar sensors. In this paper we derive methods for fusing data from multiple radar sensors in order to…
Micro-Doppler signatures are a proven modality for discriminating between drones and birds, but their reliability degrades in low-SNR, data-constrained settings where deep learning models often fail. This paper presents a systematic study…