Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 3130-3135· 0 citations· 27 references
Abstract
The increasing density of unmanned aerial systems (UAS) in urban low-altitude airspace introduces significant safety and security challenges, particularly for detecting non-cooperative drones in environments where RADAR (Radio Detection and Ranging) deployment is impractical. This paper presents a distributed, artificial intelligence (AI)-enabled multi-sensor surveillance framework integrating visual, acoustic, and radio frequency (RF) sensing through weighted decision-level fusion.Each sensing modality is processed using dedicated deep learning models, while a decision-level fusion mechanism combines predictions based on confidence scores and reliability weights. The modular architecture enables asynchronous communication through a publisher–subscriber paradigm, supporting distributed deployment and resilience under partial sensor degradation.The system is evaluated through both laboratory experiments and simulated urban environments with varying complexity. Results demonstrate that the proposed framework achieves over 90% detection precision, maintains false positive rates below 10%, and supports real-time processing exceeding 50 Hz. Furthermore, the fusion strategy effectively mitigates performance degradation in individual sensing modalities, particularly under noisy or obstructed conditions.These results highlight the potential of AI-based distributed sensor fusion systems as a scalable and cost-effective solution for real-time drone surveillance in smart urban airspace, contributing to resilient monitoring within emerging U-space ecosystems.
Experimental results demonstrate that the proposed sequential pipeline preserves high specificity while reducing missed detections compared to radar-only processing and indicates that sequential processing can offer a viable alternative to parallel fusion for edge-based drone detection under SNR and computing constraints.
Faizal Mohd Amin Sharifuldin, N. E. Abdul Rashid, Mohd Adli Md Ali et al.· Sensing and Imaging· 0 citations
The rapid growth in the use of unmanned aerial vehicles (UAVs) in commercial and military applications, along with the increasing accessibility of these technologies, has created new challenges for critical infrastructure security, airspace protection, and public safety. In this context, the development of effective methods for detecting UAVs has become crucial for security and defense applications. This paper proposes an artificial intelligence-based framework for UAV detection using radio spectrum sensing methods. The training and evaluation pipeline for the AI model uses a custom dataset consisting of 36,000 RF spectrograms in the time-frequency domain. The dataset was divided into two classes: drone, which includes RF signals from 6 commercial UAVs acquired in different operating modes, and no drone, which includes signals acquired in indoor and outdoor environments. The system has been tested in real-world dynamic scenarios at various distances in congested wireless environments characterized by high RF traffic. The experimental results achieved an accuracy of over 94% in real-world operating scenarios. The obtained results show a high level of performance, highlighting the system’s potential for real-world applications in live RF monitoring and UAV sensing.
Alexandrin Gutu, A. Lavric, Valentin Popa et al.· IEEE Access· 0 citations
The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.
Weijie Yuan, G. Sun, Jiacheng Wang et al.· IEEE Journal on Selected Top...· 0 citations
Multi-source heterogeneous sensor data fusion serves as the core technology for environmental perception systems in intelligent vehicles, playing a decisive role in ensuring driving safety. To address the interference issues of sensor detection features in complex environments, this study systematically analyzes the failure mechanisms of multimodal sensors and proposes an innovative distributed data fusion strategy. The method establishes a collaborative framework of wavelet analysis and federated filtering for data preprocessing, and develops an environment-adaptive feature-level fusion algorithm with real-time calibration drift compensation. By dynamically evaluating the effectiveness of multi-sensor features, it enables intelligent interference identification and fusion weight optimization in complex scenarios. Real-vehicle experiments demonstrate that under extreme environmental conditions such as rain, fog, and strong light, the proposed solution improves target recognition accuracy, meets real-time perception latency constraints, and significantly enhances the robustness of environmental perception systems.
Bixin Cai, Dongjuan Wei, Ye Mei et al.· Proceedings of the Instituti...· 0 citations
This paper proposes a distributed sensing framework for Cell-Free (CF) Integrated Sensing and Communications (ISAC) aimed at efficient target detection and classification. To mitigate fronthaul overhead, we implement a decentralized architecture where Access Points (APs) perform local detection and Radar Cross-Section (RCS) estimation, forwarding only refined metrics to a Central Processing Unit (CPU). We adapt the Partially Informed Maximum A Posteriori Ratio Test (MAPRT) detector to a monostatic scenario, incorporating an AP selection strategy tailored for the realistic 3GPP Urban Macro (UMa) channel model. Beyond binary detection, the framework integrates a multi-stage classification layer based on Gradient Boosting Machines and Recursive Bayesian Classification (RBC). By leveraging empirical mmWave RCS measurements of commercial drones, our approach enables high-accuracy Unmanned Aerial Vehicle (UAV) recognition. Simulation results demonstrate that the proposed framework achieves robust detection and classification performance even in sparse AP configurations. Notably, we show that exploring temporal diversity through RBC allows a reduced number of APs to match the accuracy of denser deployments over time, offering a scalable and communicationefficient solution for 6G environmental awareness.
Marlon da Silva Borges, Roberto B. Di Renna, Victor Fernandes· International Mediterranean...· 0 citations
Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe challenges: (1) scalability bottlenecks as fleet sizes expand; and (2) multi-timescale decision heterogeneity between macro task dispatching and micro velocity control. To tackle these, we formalize the problem as SensUAV and propose a Two TimeScale Reinforcement Learning framework (TSRL). Specifically, TSRL separates decision-making into two cooperative layers. At the macro level, a task-embedding sensing dispatcher handles scalability by separately encoding distinct task features and sequentially evaluating UAV suitability before task selection. At the micro level, a wind-aware velocity controller learns fine-grained velocity scheduling to adapt to dynamic environmental variations. Extensive experiments on real-world datasets demonstrate that TSRL significantly outperforms baselines, achieving average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.
Ouyang Xin, Songxin Lei, Xusen Guo et al.· 0 citations
