Navigation of a UAV Network for Optimal Surveillance of a Group of Ground Targets Moving Along a Road

With the rapid increase of vehicles in recent years, traffic surveillance becomes a crucial issue of traffic management. Since the traditional static sensor-based surveillance system can only passively monitor traffic, this paper considers the usage of unmanned aerial vehicles (UAVs), which can proactively conduct traffic surveillance thanks to the excellent mobility of UAVs. Specifically, we consider the navigation problem of a network of UAVs to effectively monitor a group of ground targets which move along a curvy road. A surveillance optimization problem is stated, and a distributed navigation algorithm for the UAV network is developed. It is proved that the proposed algorithm is locally optimal. Simulations confirm the effectiveness of the proposed navigation algorithm

Aerial Surveillance in Cities: When UAVs Take Public Transportation Vehicles

This paper considers using unmanned aerial vehicles (UAVs) to survey important sites across a city. When the sites are relatively far from the UAVs' depot, the UAVs may not be able to reach many of the sites. Suppose that a UAV can take public transportation vehicles (PTVs) like a passenger. Then, it may reach a site that is unreachable by flying only. Based on this UAV-PTV scheme, we investigate a task-UAV assignment problem, which assigns a set of surveillance tasks to UAVs. We formulate a mixed-integer linear programming (MILP) problem that minimizes the overall energy consumption of UAVs, subject to that every site is surveyed by a certain number of UAVs during a given time window, and all UAVs successfully return to the depot. Considering that this problem is NP-hard, we present two sub-optimal solutions. The first solution orders the surveillance tasks according to the starting times of their time windows. Then, starting from the earliest one, it assigns the tasks one by one to UAVs. The second solution breaks the tasks into small non-overlapping groups. It then assigns tasks to UAVs group by group. The former solution quickly addresses the assignment problem, but it lacks the overall management of UAV resources. The latter improves this by assigning a group of tasks simultaneously, and it can control the computation complexity by limiting the group size. The comparison with the brute force method shows that the proposed solutions can achieve competitive performance in a reasonable time. Note to Practitioners-Unmanned aerial vehicles (UAVs) have been widely used in surveillance missions. However, one challenge practitioners often meet is the limited flight duration. Commercial UAVs are in general powered by the onboard battery. Due to the restriction of payload, the battery capacity is constrained, which limits the UAVs' operation time. In this paper, we present the approach exploiting public transportation vehicles (PTVs). In our design, a UAV can take a public transportation vehicles such as buses, trams and trains on the roof and transfer between vehicles when necessary. With this UAV-PTV collaboration scheme, we consider how to efficiently assign surveillance tasks to UAVs. Due to the NP-hardness of the considered problem, two suboptimal algorithms are presented

Robust PLL Synchronization Unit for Grid-Feeding Converters in Micro/Weak Grids

A grid-feeding voltage source converter (GFD-VSC) requires a phase-locked loop (PLL) synchronization unit to be connected to the grid. The PLL critically affects the dynamic performance and stability of the GFD-VSC. In particular, a PLL with in-loop filtering, for working under distorted/polluted conditions, possesses a narrow stability margin and deficient performance in weak grid connections and fault ride-through (FRT) transients, also poor performance in frequency estimation. To address these problems, for the first time, a robust PLL with several enhanced characteristics is proposed in this paper. The robust PLL with a dynamic state feedback controller is designed using an H∞ robust control. The feedback controller is designed to improve the dynamic stability/response of the PLL, exposed to control uncertainties and exogenous disturbances, weak-grid connection, FRT transients and to improve its performance in frequency estimation. Numerical simulations validate the effectiveness of the proposed PLL

Deep Reinforcement Learning Based Joint 3D Navigation and Phase Shift Control for Mobile Internet of Vehicles Assisted by RIS-equipped UAVs

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs), notably, in the context of Internet-of-things (IoT). However, the application of UAVs, as active aerial base stations (BSs)/relays, is questionable in the fifth-generation (5G) WCNs with quasi-optic millimeter wave (mmWave) and beyond in 6G (visible light) WCNs. Because path loss is high in 5G/6G networks that attenuate, even, the line-of-sight (LoS) communicating signals propagated by UAVs. Besides, the limited energy/size/weight of UAVs makes it cost-deficient to design aerial multi-input/output BSs for active beamforming to strengthen the signals. Equipping UAVs with the reconfigurable intelligent surface (RIS), a passive component, can help to address the problems with UAV-assisted communication in 5G and optical 6G networks. We propose adopting the RIS-equipped UAV (RISeUAV) to provide aerial LoS service and facilitate communication for mobile Internet-of-vehicles (IoVs) in an obstructed dense urban area covered by 5G/6G. RISeUAV-aided wireless communication facilitates vehicle-to-vehicle/everything communication for IoVs for updating IoT information required for sensor fusion and autonomous driving. However, autonomous navigation of RISeUAV for this purpose is a multilateral problem and is computationally challenging for being optimally implemented in real-time. We intelligently automated RISeUAV navigation using deep reinforcement learning to address the optimality and time complexity issues. Simulation results show the effectiveness of the method

Deployment of Heterogeneous UAV Base Stations for Optimal Quality of Coverage

This article studies the quality of coverage of deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) after disasters or during some occasional events. In particular, we focus on the problem of minimizing the average UAV-user distance, while maintaining connectivity between the UAV-BSs and some nearby stationary base stations (SBSs). The UAV-BSs can be deployed at different altitudes, and their transmission powers may also be different. We first propose a decentralized deployment algorithm for a Line-of-Sight (LoS) scenario. This algorithm allows UAV-BSs to determine their movements based on only local information. So, it is applicable in a large scale. The local optimality and the convergence of the algorithm are proved. Moreover, we discuss how to use the algorithm in Non-LoS (NLoS) scenarios. Specifically, during its movement, each UAV-BS needs to verify the connectivity requirement as well as if a future movement will lose any already covered users. This extension guarantees that the average UAV-user distance keeps reducing during the movements of UAV-BSs. Computer simulations and comparisons with a benchmark method confirm the effectiveness of the proposed algorithms in terms of the quality of coverage

Surveillance of Remote Targets by UAVs ^∗

This paper considers using unmanned aerial vehicles (UAVs) to survey remote sites across a city. Suppose that a UAV can take public transportation vehicles (PTVs) like a passenger. Then, it may reach a site that is unreachable by flying only. Based on this UAV-PTV scheme, we investigate a task-UAV assignment problem. We formulate a mixed-integer linear programming (MILP) problem that minimizes the overall energy consumption of UAVs, subject to that every site is surveyed by a certain number of UAVs during a given time window. This problem is NP-hard, and we present a sub-optimal solution. It orders the surveillance tasks according to the time windows. Then, starting from the earliest task, it assigns the tasks one by one to UAVs. The comparison with the brute force method shows that the proposed solution can achieve competitive performance in a reasonable time

AI-based Navigation and Communication Control for a Team of UAVs with Reconfigurable Intelligent Surfaces Supporting Mobile Internet of Vehicles

Unmanned aerial vehicles (UAVs) are employed in wireless communication networks (WCNs) to improve coverage and quality. The applications of UAVs become problematic in the millimeters wave fifth-generation (5G) and beyond in the optical 6G WCNs because of two reasons: 1) higher path loss which means UAVs should fly at lower altitudes to be closer to the user's equipment; 2) complexities associated with a multi-input multi-output antenna to be incorporated in the UAV as an active aerial base station. We propose equipping UAVs with a (passive) reconfigurable intelligent surface (RIS) to resolve the issues with UAV-enabled wireless communication in 5G/6G. In this paper, the trajectory planning of the RIS-equipped UAV (RISeUAV) that renders aerial LoS service (ALoSS) is elaborated. The ALoSS facilitates vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication in obstructed dense urban environments for Internet-of-vehicles. (IoVs). To handle the nonconvexity and computation hardness of the optimization problem we use AI-based deep reinforcement learning to effectively solve the optimality and time complexity issues. Numerical simulation results assess the efficacy of the proposed method

SLAPS: Simultaneous Localization and Phase Shift for a RIS-equipped UAV in 5G/6G Wireless Communication Networks

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs). In 5G/6G WCNs, where massive muti-input multi-output (mMIMO) base stations (BSs) are operated for beamforming to address fast fading, shadowing, and blockage issues of millimeter waves (mmWave) and quasi-optic signals, the application of UAVs as active mMIMO transceivers is questionable. This is due to the prohibitive complexity of the required overhead baseband processor. Reconfigurable intelligent surface (RIS) is a complementary technology to mMIMO BSs to address the energy inefficiency and complexity of 5G/6G WCNs. Equipping UAVs with RISs, comprising passive elements, allows UAVs to remain promising gadgets for improving coverage and blockage issues in 5G/6G by reflecting in the sky and providing aerial line-of-sight (ALoS) service. Particularly, RIS-equipped UAVs (RISeUAVs) can be beneficial for ALoS vehicle-to-vehicle (V2V) communication of autonomous intelligent vehicles. However, channel estimation is prohibitive in a highly dynamic environment. In this light, accurate localization makes it feasible to use geometry information for phase shift and passive beam-steering. Also, accurate localization is required for crash avoidance and safe navigation in dense urban canyons. We propose the simultaneous localization and phase shift (SLAPS) method as a mmWave-localization technique for RISeUAVs. Simulation results prove the effectiveness of the method

Deployment of Charging Stations for Drone Delivery Assisted by Public Transportation Vehicles

To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance

Effective UAV Navigation for Cellular-Assisted Radio Sensing, Imaging, and Tracking

The paper develops a new cellular-assisted radio surveillance and tracking technique with an Unmanned Aerial Vehicle (UAV) being the mobile receiver and a static cellular ground base station (BS) being the illuminating source. Under the proposed framework, the resolution of the radio surveillance and imaging depends critically on the relative positions (or geometry) between the UAV, BS and target, as well as the instantaneous motions of the UAV and target. A novel UAV navigation law is developed to guarantee that after some time, the range resolution, the azimuth resolution and the distance between the UAV and the moving target will be below some given upper limits. Its mathematically rigorous analysis is presented. Simulations demonstrated the effectiveness of the developed navigation law