Intelligent Multimodal Multi-Sensor Fusion-Based UAV Identification, Localization, and Countermeasures for Safeguarding Low-Altitude Economy

The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become core challenges in airspace security assurance. This paper introduces an integrated UAV management and control system based on deep learning, which integrates multimodal multi-sensor fusion perception, precise positioning, and collaborative countermeasures. By incorporating deep learning methods, the system combines radio frequency (RF) spectral feature analysis, radar detection, electro-optical identification, and other methods at the detection level to achieve the identification and classification of UAVs. At the localization level, the system relies on multi-sensor data fusion and the air-space-ground integrated communication network to conduct real-time tracking and prediction of UAV flight status, providing support for early warning and decision-making. At the countermeasure level, it adopts comprehensive measures that integrate ``soft kill'' and ``hard kill'', including technologies such as electromagnetic signal jamming, navigation spoofing, and physical interception, to form a closed-loop management and control process from early warning to final disposal, which significantly enhances the response efficiency and disposal accuracy of low-altitude UAV management.

Chirp Delay-Doppler Domain Modulation: A New Paradigm of Integrated Sensing and Communication for Autonomous Vehicles

Autonomous driving is reshaping the way humans travel, with millimeter wave (mmWave) radar playing a crucial role in this transformation to enabe vehicle-to-everything (V2X). Although chirp is widely used in mmWave radar systems for its strong sensing capabilities, the lack of integrated communication functions in existing systems may limit further advancement of autonomous driving. In light of this, we first design ``dedicated chirps" tailored for sensing chirp signals in the environment, facilitating the identification of idle time-frequency resources. Based on these dedicated chirps, we propose a chirp-division multiple access (Chirp-DMA) scheme, enabling multiple pairs of mmWave radar transceivers to perform integrated sensing and communication (ISAC) without interference. Subsequently, we propose two chirp-based delay-Doppler domain modulation schemes that enable each pair of mmWave radar transceivers to simultaneously sense and communicate within their respective time-frequency resource blocks. The modulation schemes are based on different multiple-input multiple-output (MIMO) radar schemes: the time division multiplexing (TDM)-based scheme offers higher communication rates, while the Doppler division multiplexing (DDM)-based scheme is suitable for working in a lower signal-to-noise ratio range. We then validate the effectiveness of the proposed DDM-based scheme through simulations. Finally, we present some challenges and issues that need to be addressed to advance ISAC in V2X for better autonomous driving. Simulation codes are provided to reproduce the results in this paper: \href{https://github.com/LiZhuoRan0/2025-IEEE-Network-ChirpDelayDopplerModulationISAC}{https://github.com/LiZhuoRan0}.

MmWave-LoRadar Empowered Vehicular Integrated Sensing and Communication Systems: LoRa Meets FMCW

The integrated sensing and communication (ISAC) technique is regarded as a key component in future vehicular applications. In this paper, we propose an ISAC solution that integrates Long Range (LoRa) modulation with frequency-modulated continuous wave (FMCW) radar in the millimeter-wave (mmWave) band, called mmWave-LoRadar. This design introduces the sensing capabilities to the LoRa communication with a simplified hardware architecture. Particularly, we uncover the dual discontinuity issues in time and phase of the mmWave-LoRadar received signals, rendering conventional signal processing techniques ineffective. As a remedy, we propose a corresponding hardware design and signal processing schemes under the compressed sampling framework. These techniques effectively cope with the dual discontinuity issues and mitigate the demands for high-sampling-rate analog-to-digital converters while achieving good performance. Simulation results demonstrate the superiority of the mmWave-LoRadar ISAC system in vehicular communication and sensing networks.