More Like this

1. Testing and Evaluation of Radio Frequency Immunity of Unmanned Aerial Vehicles For Bridge Inspection

   https://doi.org/10.1109/AERO50100.2021.9438457  

   Mahama, Edward; Walpita, Thisara; Karimoddini, Ali; Eroglu, Abdullah; Goudarzi, Navid; Cavalline, Tara; Khan, Mubbashar (June 2021, 2021 IEEE Aerospace Conference)

   Recent technological advances have led to an increase in the adoption of Unmanned Aerial Vehicles (UAVs) in a variety of use-case scenarios. In particular, Departments of Transportation in several states in the United States have been exploring the use of UAVs for bridge and infrastructure inspections to improve safety and reduce the costs of the inspection process. UAVs are remotely piloted from a cockpit or a ground station via radio channels. The UAV's state information and payload information are also transmitted to the cockpit/ground station via radio frequency (RF) signals. The RF channels that are commonly used by most UAVs are 72-73, 902-928 and 2400-2483.5 MHz bands, which is also shared by several other communication protocols such as, WiFi and ZigBee networks, and therefore, the interference effects with the other services on the UAV's operation performance cannot be overlooked, particularly to maintain the minimum distance from the close by surfaces while flying alongside and underneath the bridges to achieve the best results. The loss of signal or even signal strength during such close flights can cause damage to the UAV. Especially while inspecting the bridges located in the urban areas that involve a lot of RF communication around due to presence of sever RC devices providing different services. Conventional Electromagnetic Compatibility (EMC) adherence requirements imposed on electronic systems are not adequate for UAVs due to their airborne nature and the presence of the other RF sources in the environment. Thus, in this work, we investigate the compliance of EMC requirements by designing and conducting field experiments to expose the UAVs to electromagnetic interference and distortions that are likely to be encountered during the UAV operation. The results of this work will enable us to assess the level of RF immunity of the general-purpose UAVs to aid in the selection of a suitable UAV platform for bridge inspection and develop safety procedures for minimizing the impact of RF interference. 

   more » « less
2. Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

   https://doi.org/10.3390/s23239524  

   Syed, Muhammad Hashir; Singh, Maninderpal; Camp, Joseph (December 2023, Sensors)

   UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance. 

   more » « less
3. Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning

   https://doi.org/10.1109/ICASSP39728.2021.9414985  

   Xu, Chengtao; He, Fengyu; Chen, Bowen; Jiang, Yushan; Song, Houbing (June 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. 

   more » « less
4. Architecture, Classification, and Applications of Contemporary Unmanned Aerial Vehicles

   https://doi.org/10.1109/MCE.2021.3063945  

   Alghamdi, Yousef; Munir, Arslan; La, Hung Manh (March 2021, IEEE Consumer Electronics Magazine)

   null (Ed.)

   Unmanned aerial vehicles (UAV) have been gaining significant attention in recent times as they are becoming increasingly accessible and easier to use. The advancements in flight controller technology have enabled users to fly a recreational UAV without any previous flight experience. UAVs are used in a variety of applications, ranging from civilian tasks, law enforcement, and rescue applications, to military reconnaissance and air strike missions. This article serves as an introduction to UAV systems' architecture, classification, and applications to help researchers and practitioners starting in this field get adequate information to understand the current state of UAV technologies. The article starts by inspecting the UAVs' body configuration styles and explains the physical components and sensors that are necessary to operate and fly a UAV system. The article also provides a comparison of several components for state-of-the-art UAVs. The article further discusses different propulsion methods and various payloads that could be mounted on the UAV. The article then explores the classification of UAVs followed by the application of UAVs in different domains, such as recreational, commercial, and military. Finally, the article provides a discussion of futuristic technologies and applications of UAVs along with their associated challenges. 

   more » « less
5. Counter UAV Swarms: Challenges, Considerations, and Future Directions in UAV Warfare

   https://doi.org/10.1109/MWC.003.2400047  

   Sherman, Michelle; Shao, Sihua; Sun, Xiang; Zheng, Jun (January 2024, IEEE Wireless Communications)

   Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results. 

   more » « less