TY - JOUR
T1 - A Survey of Indoor and Outdoor UAV-Based Target Tracking Systems
T2 - Current Status, Challenges, Technologies, and Future Directions
AU - Alhafnawi, Mohannad
AU - Bany Salameh, Haythem A.
AU - Masadeh, Ala'eddin
AU - Al-Obiedollah, Haitham
AU - Ayyash, Moussa
AU - El-Khazali, Reyad
AU - Elgala, Hany
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - Due to their distinctive features, unmanned aerial vehicles (UAVs) have been recently exploited to support a wide range of applications. The features include low maintenance cost, compact size, and excellent capability of maneuvering. In particular, UAVs have the potential capabilities to support different technologies such as Internet-of-things (IoT) devices, sensors, cameras, and systems, thus, performing civilian, target tracking, industrial, and military applications. Specifically, target tracking has been recently configured as one of the most attractive applications of UAVs. With this, UAVs estimate and detect the behavior or locate a moving or a stationary item. Accordingly, several research efforts have been conducted to investigate the promising capabilities of UAVs in target-tracking missions, including indoor and outdoor tracking missions. This paper surveys UAV-based target tracking and monitoring for indoor and outdoor environments, where the deployment scenarios of such UAV-based systems are characterized and investigated. Furthermore, we discuss a set of practical design challenges of UAV-based target tracking systems, and thus, we provide a set of potential solutions to deal with these challenges. Specifically, we present a set of recent enabling technologies that might be integrated into UAV target tracking systems, including machine learning (ML), cloud computing, and emerging fifth-generation (5G) technologies. We also demonstrate a use-case scenario in which ML is used to facilitate indoor target tracking and monitoring. Finally, future research directions are outlined that can help in improving the efficiency of the UAV-based target-tracking systems.
AB - Due to their distinctive features, unmanned aerial vehicles (UAVs) have been recently exploited to support a wide range of applications. The features include low maintenance cost, compact size, and excellent capability of maneuvering. In particular, UAVs have the potential capabilities to support different technologies such as Internet-of-things (IoT) devices, sensors, cameras, and systems, thus, performing civilian, target tracking, industrial, and military applications. Specifically, target tracking has been recently configured as one of the most attractive applications of UAVs. With this, UAVs estimate and detect the behavior or locate a moving or a stationary item. Accordingly, several research efforts have been conducted to investigate the promising capabilities of UAVs in target-tracking missions, including indoor and outdoor tracking missions. This paper surveys UAV-based target tracking and monitoring for indoor and outdoor environments, where the deployment scenarios of such UAV-based systems are characterized and investigated. Furthermore, we discuss a set of practical design challenges of UAV-based target tracking systems, and thus, we provide a set of potential solutions to deal with these challenges. Specifically, we present a set of recent enabling technologies that might be integrated into UAV target tracking systems, including machine learning (ML), cloud computing, and emerging fifth-generation (5G) technologies. We also demonstrate a use-case scenario in which ML is used to facilitate indoor target tracking and monitoring. Finally, future research directions are outlined that can help in improving the efficiency of the UAV-based target-tracking systems.
KW - artificial intelligence (AI)
KW - drones
KW - indoor/outdoor deployment
KW - Target tracking systems
KW - uncertain movement
UR - http://www.scopus.com/inward/record.url?scp=85164451979&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3292302
DO - 10.1109/ACCESS.2023.3292302
M3 - Article
AN - SCOPUS:85164451979
SN - 2169-3536
VL - 11
SP - 68324
EP - 68339
JO - IEEE Access
JF - IEEE Access
ER -