In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.
With the development of space missions, the increasing communication data volume, the diversity of mission-specific interfaces, and the growing security needs, the costs and system complexity of ground stations have increased. The architecture of the existing ground terminal is outdated and the operation and maintenance are labor-intensive, so the terminal components are unable to meet the demand of multi-satellite TT&C in the future. In this paper, a Shared Satellite Ground Station(SSGS) using user-oriented virtualization technology is proposed for coping with complex multi-satellite TT&C in the future. Based on a pool architecture, SSGS utilizes baseband processor virtualization and software-defined networking to implement resource sharing and a network that fuses high-speed and low-speed data streams with customization control. The case analysis verifies that when SSGS has a virtualization rate greater than 1, the execution rate of tasks is much larger than that of traditional ground stations. The case analysis also verifies that the virtualization rate of current commercial equipment is no less than 1.25 through equipment testing. INDEX TERMS Satellite ground station, baseband processor virtualization, software-defined network, TT&C. I. INTRODUCTION Ground stations are used for receiving payload data and Spacecraft telemetry, tracking, and command (TT&C). Traditional ground station networks generally belong to the government or the military, e.g.NASA's STDN (Spaceflight Tracking and Data Network), the United States' AFSCN (Air Force Satellite Control Network) and ESA's ESTRACK. Whereas with the development of civil and commercial aerospace, e.g. Space Imaging, Orbital Space Imaging, OneWeb and Planet Labs, more space launch missions and satellites in orbit have brought considerable challenges to ground stations, i.e. it is difficult for traditional ground station networks to provide flexible services for numerous commercial satellites. The contradiction between limited ground stations and the increasing demand has become increasingly prominent. Hence, the development of commercial ground stations becomes an inevitable trend [1], [2]. Civil and commercial ground station networks distributed around the world came into being. GENSO (Global Educational Network for Satellite Operations) is a network The associate editor coordinating the review of this manuscript and approving it for publication was Rosario Pecora. of university and amateur ground stations managed by ESA., which accommodates numerous ground stations and improves educational spacecraft communication[3]. NASA, JPL and the Center for EUV Astrophysics have evaluated a commercially available ground station for telemetry reception, processing, and routing of data over a commercial, secure data line[4]. SSC (Swedish Space Corporation)'s Pri-oraNet is a global ground station network consisting of a network management center, SSC's core stations, and cooperative stations all over the world. It provides reliable mission management, TT&C (Tracking, Telemetry ...
Group target tracking (GTT) is a promising approach for countering unmanned aerial vehicles (UAVs). However, the complex distribution and high mobility of UAV swarms may limit GTTs performance. To enhance GTT performance for UAV swarms, this paper proposes potential solutions. An automatic measurement partitioning method based on ordering points to identify the clustering structure (OPTICS) is suggested to handle non-uniform measurements with arbitrary contour distribution. Maneuver modeling of UAV swarms using deep learning methods is proposed to improve centroid tracking precision. Furthermore, the group’s three-dimensional (3D) shape can be estimated more accurately by applying key point extraction and preset geometric models. Finally, optimized criteria are proposed to improve the spawning or combination of tracking groups. In the future, the proposed solutions will undergo rigorous derivations and be evaluated under harsh simulation conditions to assess their effectiveness.
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