Related papers: A Framework for UAV-based Distributed Sensing Unde…
The application domains of civilian unmanned aerial systems (UASs) include agriculture, exploration, transportation, and entertainment. The expected growth of the UAS industry brings along new challenges: Unmanned aerial vehicle (UAV)…
The dominating waveform in 5G is orthogonal frequency division multiplexing (OFDM). OFDM will remain a promising waveform candidate for joint communication and sensing (JCAS) in 6G since OFDM can provide excellent data transmission…
Recently, passive unmanned aerial vehicle (UAV) localization has become popular due to mobility and convenience. In this paper, we consider a scenario of using distributed drone cluster to estimate the position of a passive emitter via…
This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search…
The widespread use of unmanned aerial vehicles (UAVs) in low-altitude airspace has raised significant safety and security concerns, motivating the development of reliable non-cooperative UAV surveillance technologies. Integrated sensing and…
We propose in this work a radar detection system for orthogonal-frequency division multiplexing (OFDM) transmission. We assume that the transmitting antenna Tx is colocated with a monostatic radar. The latter knows the transmitted signal…
Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role for low-altitude economy with a wide range of applications like precise agriculture, aerial delivery and surveillance. Integrated sensing and communication…
The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment.…
In future sixth-generation (6G) mobile networks, radar sensing is expected to be offered as an additional service to its original purpose of communication. Merging these two functions results in integrated sensing and communication (ISAC)…
Aerial object detection using unmanned aerial vehicles (UAVs) faces critical challenges including sub-10px targets, dense occlusions, and stringent computational constraints. Existing detectors struggle to balance accuracy and efficiency…
This paper presents a novel efficient method for spatial monitoring of the distribution of correlated field signals, such as temperature, humidity, etc. using unmanned aerial vehicles (UAVs). The spatial signal is compressed to its…
To meet the requirements for managing unauthorized UAVs in the low-altitude economy, a multi-modal UAV trajectory prediction method based on the fusion of LiDAR and millimeter-wave radar information is proposed. A deep fusion network for…
In this paper, we investigate the uplink signal detection approaches in the cell-free massive MIMO systems with unmanned aerial vehicles (UAVs) serving as aerial access points (APs). The ground users are equipped with multiple antennas and…
Multistatic collaborative sensing eliminates self-interference, achieves spatial diversity gains, and enables wide-range seamless integrated sensing and communication (ISAC). However, conventional data fusion methods suffer from severe…
This paper considers the beamforming optimization for sensing a point-like scatterer using a bistatic multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) radar, which could be part of a joint…
Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions,…
Unmanned Aerial Vehicles (UAVs) hold immense potential for critical applications, such as search and rescue operations, where accurate perception of indoor environments is paramount. However, the concurrent amalgamation of localization, 3D…
Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems…
This paper investigates federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency and providing convergence analysis. In this framework, UAVs are distributed throughout the…
This paper introduces a novel unsupervised jamming detection framework designed specifically for monostatic multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) radar systems. The framework leverages echo…