Recently, the Low-Altitude Industry Alliance released the Reference Architecture of the Low-Altitude Intelligent Networked System (2024 Edition) report, which outlines the basic content of the developmental evolution stages, components, and system framework of the low-altitude intelligent networked system. This document provides a reliable reference and foundation for the development of the low-altitude intelligent networked system. However, as a framework-based report, the report focuses on presenting the key components of the low-altitude intelligent networked system in the most concise and precise manner, lacking detailed descriptions of the underlying scientific methods, theoretical foundations, and implementation approaches. This paper comprehensively elaborates on the current state of development, design concepts, system logic, and key technologies of the low-altitude intelligent networked system based on the report. It aims to further analyze and interpret the content of the report, providing a scientific theoretical reference for the subsequent development and construction of the low-altitude intelligent networked system.

Radio Frequency Fingerprint Identification (RFFI) technology provides a means of identifying spurious signals. This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast (ADS-B) signal spoofing problems. However, the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set. This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy. First, a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set. The system possesses the following functionalities: data collection, data parsing, feature extraction, and identity recognition. Subsequently, a two-dimensional Time-Frequency Feature Diagram (TFFD) is proposed as a signal pre-processing method. The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model. Finally, incorporating a frequency offset layer into the Swin Transformer architecture, a novel recognition network framework is proposed. This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADS-B signals. Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86% in closed-time set and 84.33% in open-time set, surpassing other algorithms.

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability. However, the space-based ADS-B system faces new problems such as extremely low Signal-to-Noise Ratio (SNR) and serious co-channel interference, which result in long update intervals. To minimize the position message update interval at an update probability of 95% with full coverage constraint, this paper presents an optimization model of digital multi-beamforming for space-based ADS-B. Then, a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy. The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam. Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods, and DECCG_A&A is superior in convergence to the Genetic Algorithm (GA) as well as the coevolution algorithms using other grouping strategies. Overall, the proposed optimization model and method can significantly reduce the update interval, thus improving the surveillance performance of space-based ADS-B for air traffic control.