Satellite-Assisted UAV Trajectory Control in Hostile Jamming Environments

Abstract

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.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

“…These optimization-based methods typically require prior knowledge of the jammer, and lack the ability to adapt to different jamming environments, e.g., in which the jammer's location is changed, or jamming is performed in a time-varying fashion. To tackle this challenge, the authors in [17] developed a reinforcement learning (RL) based automatic flight control algorithm to perform UAV 2 trajectory design in a coordinated satellite-UAV communication system in the presence of ground jamming attacks. The jammer launched attacks according to a jamming probability.…”

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

“…Minimization of mean squared error (MSE) in a scenario where the UAVs are flying and collecting data from sensors is presented in Khosravi and Samadi. 134 Automated inspection of railway systems with UAV aerial images along with fully decoupled convolution filters is proposed in Tong et al 135 Secure UAV communication with trajectory control is modeled in Tang et al 113 and Han et al 114 To identify the UAVs, an e-Sim-based authentication protocol is presented in Shoufan et al 136 The characteristics of Google's Loon project is further studied in Serrano et al 137 3D localization, interference management, channel modeling, and handover management are the challenging research areas to consider for applications with UAV as UE, whereas for BS, applications face challenges in optimal 3D deployment, resource allocation, network planning, and trajectory optimization. 138…”

An unsupervised aerial tracking method of camouflaged targets in complex environments

“…UAVs can provide technical support for reconnaissance and early warning, battlefield surveillance and target tracking, which is an important way to obtain intelligence information and enhance battlefield situational awareness. Strategic UAVs can carry out long periods of continuous reconnaissance and surveillance of the enemy [1,2], with the characteristics of dynamic and real-time information, and can effectively detect enemy time-sensitive targets and targets with irregular activities.…”