Multiple Small Unmanned Aerial Vehicles Research Articles

DOA and subarray-interval estimation for arbitrarily distributed UAV swarm system

In this paper, DOA and subarray-interval estimation are considered and applied to arbitrarily distributed unmanned aerial vehicle (UAV) swarm system, in which multiple small UAVs containing uniform linear array (ULA) are divided by unknown intervals because of dynamic moving. Three parameters are taken to indicate the steering vector, namely, the direction of arrivals (DOAs) of target users, the intervals of UAVs, and the orientation angles of UAVs. The orientation angles are first estimated with an auxiliary user and the DOAs are obtained through a search free rooting method, despite the intervals among the UAVs. Afterwards, the intervals among UAVs can also be calculated via exhaustive when the number of target users are no less than three. We further develop a low-complex method to reduce the computational complexity during subarray-interval estimation. The deterministic Cramer-Rao bound (CRB) of the DOA, orientation angle and subarray-interval can be inferred in a closed form. Eventually, numerical instances are cited to verify the research results.

FLIGHT DYNAMICS AND STABLE CONTROL ANALYSES OF MULTI-BODY AIRCRAFT

Multi-body aircraft is a new concept aircraft consisting of multiple small unmanned aerial vehicles hinged-connected by wing tips. This concept aircraft can integrate the performance and advantages of two types of flying platforms: small unmanned aerial vehicles and high-altitude long endurance unmanned aerial vehicles. It has great development potential. Based on the Newton-Euler equation and on the lifting line method, it establishes a multi-body aircraft flight dynamics model, and analyzes the trim and stability characteristics of the multi-body aircraft system that allowing the relative degree of freedom of rolling motion with the dual-aircraft combination. The results show that, different from the traditional configuration aircraft, the multi-body aircraft system has an unstable relative motion flight mode, which is dominated by relative rolling motion. This configuration aircraft cannot fly stably in an uncontrolled situation. Based on the completion of the dynamics modeling, the Proportion-Integration-Differentiation control method is used for stable control. The stable control loop is designed separately for each single aircraft to achieve the purpose of stable flight. The simulation results show that the control method is effective and can quickly stabilize the divergent flight dynamics system.

Secure Communication for Multiquadrotor Networks Using Ethereum Blockchain

Ethereum blockchain is a powerful, open-source technology for creating decentralized and secure information sharing systems. The main contribution of this article is the experimental validation of an Ethereum blockchain-based software and hardware architecture that enables secure communication for multiple small unmanned aerial vehicles (sUAVs). The experiments involved three DJI M100 quadrotors that shared images captured during flight based on smart contracts created using Ethereum’s Turing complete programming language. The smart contract was designed so that only the intended recipient sUAV could access a specific image. The effect of image size, difficulty level, and consensus algorithms on image transfer times during flight are noted and point to the feasibility of this system in practical missions. The effects of wireless network disruptions on the Ethereum network are documented. The fully documented smart contract code is open sourced to assist readers in quick prototyping. As efforts for decentralization and security of multirobot systems continue to grow, the system architecture and implementation detailed here may serve as a guide for future research.

Channel Estimation and Self-Positioning for UAV Swarm

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

Cooperative target localization by multiple unmanned aircraft systems using sensor fusion quality

Over the past several years, researchers at the U.S. Air Force Academy developed cooperative, distributed aerial sensor networks (Pack et al. in IEEE Trans Syst Man Cybern Part B Cybern 39(4):959–970, 2009) using multiple small unmanned aerial vehicles (SUAVs) to search, detect, and locate ground targets. The use of distributed SUAVs, however, introduced a set of problems, including difficulties in reliable air-to-air communication and clock synchronization among onboard systems of multiple SUAVs. The communication problems further aggravate the synchronization problem contributing to a large target localization error. Conventional methods use multiple sensor outputs of the same target seen from different perspectives to increase the target localization accuracy. These methods are effective only when the pose errors of sensor platforms based on GPS data are modeled accurately, which is not a reasonable assumption for SUAVs, especially when SUAVs operate in an environment with wind gusts. In this paper, we propose a robust, novel technique that analyzes what we call ‘sensor fusion quality’ to assign an appropriate sensor reliability value to each set of updated sensor data. In the proposed approach, we characterize the quality of a set of newly acquired sensor data, containing a target, by examining the joint target location probability density function. The validity of the proposed method is demonstrated using flight test data.