Towards Persistent Surveillance and Reconnaissance Using a Connected Swarm of Multiple UAVs

Cho, Jeihee; Sung, Jaeyi; Yoon, JinYi; Lee, HyungJune

doi:10.1109/access.2020.3019963

Cited by 16 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the passive approach is not suitable for a swarm reconnaissance system because performing a dynamic mission is difficult. Cho et al [7] proposed an efficient network configuration method based on a distributed network according to the mission to increase communication efficiency. However, their proposed method was only verified by simulations and only considered two dimensions, which is insufficient for application to a real environment.…”

Section: A Uav Swarmmentioning

confidence: 99%

Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

et al. 2023

View full text Add to dashboard Cite

Recent developments in drone technology have led to the widespread use of unmanned aerial vehicles (UAVs). In particular, UAVs are often used in reconnaissance to detect objects such as missing persons in large areas. However, traditional systems use only one UAV to search for missing persons in a large area. In addition, object detection is performed after flight or manually because detection requires high computing power. In this paper, a reconnaissance drone system using multiple UAVs is proposed. The proposed multi-UAV reconnaissance system performs real-time object detection on each UAV. The realtime object detection results from each UAV are received by the ground control system (GCS) to stitch the images. To enable real-time object detection in individual UAVs, the filter pruning method is applied to the YOLOv5 model, and the model uses 40% fewer parameters than the existing baseline model. The lightweight YOLOv5 model achieves approximately 11.73 FPS on the Jetson Xaiver NX using a mission computer. Moreover, the proposed image stitching method enables image stitching by effectively matching features using additional information generated by UAVs. The UAV flight tests show that the proposed reconnaissance system can monitor and detect objects in real time over large areas.INDEX TERMS Image stitching, network pruning, real-time object detection, swarm flight system.

show abstract

Section: A Uav Swarmmentioning

confidence: 99%

Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

et al. 2023

View full text Add to dashboard Cite

show abstract

“…1. The large UAV, equipped with electronic reconnaissance equipment, is specially used for intercepting and recording enemy electromagnetic radiation signals from the ground [33]. Then, the reconnaissance data is uploaded to satellites for processing and analyzing.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…In this paper, the target are is meshed into grids [33]. The size of each grid is set according to the coverage range of a spot beam.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…The coordinates projected on the horizontal plane of the location at current moment and next moment are defined as l t = [x t , y t ] and l t+1 = [x t+1 , y t+1 ]. UAV can choose four motion selection: forward, backward, left, and right [33]. Then, UAV goes into the adjacent grid, limited by l t − l t+1 = 1, where l t − l t+1 represents the Manhattan distance from l t to l t+1 ,…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Satellite-Assisted UAV Trajectory Control in Hostile Jamming Environments

Liu

Wang

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Satellite and unmanned aerial vehicle (UAV) networks have been introduced as enhanced approach to provide dynamic control, massive connections and global coverage for future wireless communication systems. This paper considers a coordinated satellite-UAV communication system, where the UAV performs the environmental reconnaissance task with the assistance of satellite in a hostile jamming environment. To fulfill this task, the UAV needs to realize autonomous trajectory control and upload the collected data to the satellite. With the aid of the uploading data, the satellite builds the environment situation map integrating the beam quality, jamming status, and traffic distribution. Accordingly, we propose a closed-loop anti-jamming dynamic trajectory optimization approach, which is divided into three stages. Firstly, a coarse trajectory planning is made according to the limited prior information and preset points. Secondly, the flight control between two adjacent preset points is formulated as a Markov decision process, and reinforcement learning (RL) based automatic flying control algorithms are proposed to explore the unknown hostile environment and realize autonomous and precise trajectory control. Thirdly, based on the collected data during the UAV's flight, the satellite utilizes an environment situation estimating algorithm to build an environment situation map, which is used to reselect the preset points for the first stage and provide better initialization for the RL process in the second stage. Simulation results verify the validity and superiority of the proposed approach.

show abstract

“…This helps ensure the user quality of experience (QoE) in the network [11]. In situations of communication infrastructure has collapsed, the authors in [12] used a swarm of UAVs for emergency area search to achieve the fastest coverage and provide up‐to‐date on‐site information by optimizing the swarm flight trajectory. The literature [13] investigated the resource optimization problem of UAV sensing data transmission to mobile devices and studied the energy utility maximization problem by jointly optimizing the trajectory, velocity, and transmit power of the UAV in the system.…”

Section: Introductionmentioning

confidence: 99%

Joint mission planning and spectrum resources optimization for multi‐UAV reconnaissance

Liao

et al. 2022

IET Communications

View full text Add to dashboard Cite

In this paper, the problem of mission planning and spectrum resource allocation for cooperative reconnaissance of ground targets with multiple unmanned aerial vehicles (UAVs) is studied. A joint mission planning and spectrum resource optimization algorithm for multi-UAVs is proposed to improve the information transmission rate by reusing the spectrum of existing users. The joint optimization problem is formulated as mixed-integer nonlinear programming. The block coordinate descent (BCD) method is further applied to achieve the optimal strategies of mission planning, channel allocation, and power control. Specifically, an improved genetic algorithm (GA) combined with the successive convex approximation (SCA) is used to solve the sub-problem of mission planning. For the channel allocation sub-problem, an iterative convergence channel allocation algorithm is proposed. Numerical results show that the proposed algorithm can achieve a higher UAV transmission rate and better robustness than existing algorithms.

show abstract

Towards Persistent Surveillance and Reconnaissance Using a Connected Swarm of Multiple UAVs

Cited by 16 publications

References 29 publications

Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

Satellite-Assisted UAV Trajectory Control in Hostile Jamming Environments

Joint mission planning and spectrum resources optimization for multi‐UAV reconnaissance

Contact Info

Product

Resources

About