This survey presents a systematic and comprehensive review of UAV-based target detection, tracking, and prediction methods, spanning classical estimation models, deep learning frameworks, reinforcement learning strategies, and cooperative multi-UAV intelligence, and introduces an Artificial Intelligence-Driven Radio Access Network (AI-RAN)-enhanced conceptual framework.
Abstract
Recent advances in unmanned aerial vehicle (UAV)-based target detection and tracking increasingly rely on model-based techniques such as Kalman filtering (KF) and particle filtering (PF), as well as learning-driven approaches including deep learning and reinforcement learning. Despite these developments, existing UAV systems continue to face major challenges arising from increasing target densities, complex terrain, dynamic wireless conditions, communication limitations, and restricted onboard computational and energy resources. These constraints significantly affect tracking accuracy, real-time responsiveness, and service reliability, particularly in resource-constrained and rapidly changing environments. To address these challenges, this survey presents a systematic and comprehensive review of UAV-based target detection, tracking, and prediction methods, spanning classical estimation models, deep learning frameworks, reinforcement learning strategies, and cooperative multi-UAV intelligence. The survey further emphasizes the integration of UAV-assisted edge computing with the Open Radio Access Network (O-RAN) framework, where the RAN Intelligent Controller (RIC), together with xApps and rApps, enables scalable, low-latency, and adaptive communication optimization between aerial and terrestrial nodes. Building on this foundation, the survey introduces an Artificial Intelligence-Driven Radio Access Network (AI-RAN)-enhanced conceptual framework that combines particle filtering, Q-Learning (QL) control, and AI-driven RAN optimization to enable joint communication, computation, and control. The proposed architectural perspective demonstrates how multi-modal sensor fusion and distributed edge intelligence can jointly improve tracking robustness, responsiveness, and energy efficiency. Finally, the survey highlights open challenges and future research directions toward fully autonomous, scalable, and network-aware UAV tracking systems for emerging 6G and edge-AI environments.
This paper aims to provide a comprehensive review of artificial intelligence (AI)-based navigation techniques for unmanned aerial vehicles (UAVs) operating in GPS-denied environments. It highlights the limitations of traditional satellite-based navigation in indoor, urban and dense environments, and explores how AI-driven approaches can enhance autonomous navigation reliability and accuracy.
The study reviews literature published between 2020 and 2025, focusing on AI-based navigation frameworks. It analyzes methods such as deep learning, reinforcement learning, visual/inertial SLAM, Kalman filtering and multi-sensor fusion. A comparative evaluation is conducted based on algorithm design, sensor configurations, computational requirements and validation methodologies used in existing research.
The review identifies a growing trend toward hybrid navigation architectures combining traditional estimation techniques with AI-based models. It finds that sensor fusion and learning-based perception significantly improve navigation in GPS-denied environments. However, challenges such as high computational requirements, limited onboard processing capabilities and data inefficiency in reinforcement learning remain key barriers.
This paper provides an up-to-date and structured review of AI-based UAV navigation specifically focused on GPS-denied environments. It offers a detailed comparison of existing approaches and highlights emerging trends such as hybrid architectures and Edge AI integration, providing valuable insights and future research directions for researchers and practitioners.
N. Kalaimani, R. Saran, G. Vignesh· International Journal of Int...· 0 citations
In this paper, we propose an energy-efficient and reliable communication system for non-terrestrial networks deployed in dynamic GPS-denied wireless environments, enabled by a Vision--Inertial Tracking-Assisted UAV Communication (VIT-UAVCom) system. To the best of our knowledge, this is the first work to exploit onboard UAV cameras and IMU sensors for UAV-assisted communications. We consider a complete VIT-UAVCom system that incorporates the key design parameters while explicitly accounting for system noise and residual tracking inaccuracies. Building on this framework, we formulate an optimization problem for jointly designing the UAV trajectory and communication performance to improve propulsion energy efficiency, reduce outage probability, and enhance physical-layer security. We then develop a dedicated solution framework to efficiently compute near-optimal trajectory and communication control actions in dynamic scenarios. Furthermore, to enable real-time implementation, we propose and evaluate three optimizers, namely linear search (LS), binary search (BS), and genetic search. Our numerical results demonstrate that our proposed VIT-UAVCom framework significantly outperforms the K-means benchmark in terms of energy consumption while maintaining robust secrecy performance and reliable user coverage. Specifically, our proposed framework improves the energy efficiency by 144% compared to the benchmark. Interestingly, our results also show that, compared with the LS, the BS reduces the computational time by approximately 50%.
Abdallah S. Ghazy, Hussein A. Ammar, James H. Bayes et al.· 0 citations
This review provides a comprehensive synthesis of traditional and artificial intelligence (AI)-based techniques across the complete autonomous UAV navigation pipeline, including environmental perception, localization and mapping, path planning and obstacle avoidance, and motion control, together with commonly used datasets, simulation platforms, and evaluation practices.
T. Mahmood, Ali Ahmed Mirza· Scientific Journal of Engine...· 0 citations
Cooperative target tracking has become a fundamental capability for unmanned underwater vehicles (UUVs) in applications such as maritime surveillance, environmental monitoring, underwater infrastructure inspection, and defense operations. Compared with single-platform tracking, cooperative target tracking enables UUVs to achieve enhanced perception accuracy, wider spatial coverage, improved robustness, and persistent observation through information sharing and coordinated decision-making. However, the unique characteristics of underwater environments, including limited-bandwidth acoustic communication, long transmission delays, intermittent connectivity, environmental disturbances, and constrained platform maneuverability, introduce significant challenges to target state estimation, cooperative control, mission coordination, and system deployment. This paper presents a comprehensive review of recent advances in cooperative target tracking for UUVs. First, the overall architecture and key enabling technologies are introduced. Subsequently, recent progress in target state estimation, collaborative decision-making, and cooperative control is systematically reviewed, with particular emphasis on communication-constrained information fusion, distributed coordination, and learning-based intelligent decision-making. Emerging technologies, including digital twins, embodied intelligence, communication-aware artificial intelligence, and large-scale heterogeneous swarm collaboration, are further discussed from both algorithmic and engineering perspectives. Finally, current research challenges and emerging technologies are summarized to provide insights for future development.
Rong Wei, Peng Yu, Dong Xiao et al.· Journal of Marine Science an...· 0 citations
Existing RF-based UAV detection methods achieve high accuracy in identifying UAV presence and model type, yet they largely characterize UAVs through static signal attributes, offering limited insight into what an identified UAV is actually doing. This gap constrains their practical value for airspace monitoring and threat assessment. This paper presents a multi-feature RF-based framework for UAV behavioral state recognition and short-horizon behavior prediction, built upon a set of newly defined behavioral indicators, namely, spectral dynamics, signal-power-based motion trend, and communication density, integrated through a dedicated time-series modeling module. To support this study, we construct UAV-BehaviorRF, a new dataset with fine-grained behavioral annotations collected via a scripted multi-state flight protocol across eight UAV models. Experiments on UAV-BehaviorRF and the public DroneRFa dataset show that the proposed framework achieves accurate behavioral state recognition and reliable state-transition prediction, while remaining robust under interference and real-world conditions and maintaining real-time processing suitable for resource-constrained deployment.
Runze Mao, Teng Wu, Sheng-Jun Wei et al.· Drones· 0 citations
The cooperative tracking of multiple ground targets by multiple UAVs remains challenging under partial observability, limited communication, and obstacle constraints, owing to complex target association, difficult task handover, strong coupling among low-level continuous control decisions, and unstable critic value estimation. To address these issues, this paper proposes a hierarchical-guidance and Gaussian-process-corrected multi-agent proximal policy optimization method, termed HGP-MAPPO. Built upon the centralized-training and decentralized-execution paradigm, HGP-MAPPO introduces low-frequency task-guidance signals derived from target-association information, task handover and recovery cues, task priorities, and desired observation geometry. These guidance signals are incorporated as conditional inputs into the low-level actor–critic framework, thereby reducing the policy learning difficulty in jointly handling target tracking, occlusion recovery, obstacle avoidance, and smooth control. Moreover, to alleviate local estimation bias in the neural-network critic under complex partially observable conditions, a Gaussian-process-based residual correction mechanism is designed. Specifically, the posterior mean is used to compensate for value residuals, while the posterior uncertainty adaptively regulates the correction intensity, improving the stability of value evaluation and policy optimization. A sparse inducing-point approximation is adopted to control the training-stage computational cost, while the Gaussian-process module is removed during decentralized execution and, therefore, introduces no additional online inference overhead. Experiments are conducted in standard-obstacle and densely obstructed multi-UAV multi-target tracking scenarios, with DDPG-MHSA, MAPPO, MADDPG, and MATD3 adopted as baselines. The experimental results demonstrate that HGP-MAPPO achieves faster training convergence, higher average episode rewards, and improved target retention rates. It also effectively reduces UAV–target distance fluctuations and the mean absolute temporal-difference (TD) error. Ablation studies further confirm the contributions of task-guidance signals, Gaussian-process residual correction, and uncertainty-aware weighting to cooperative tracking performance and training stability.
