With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability.

The rapid proliferation of low-altitude unmanned aerial vehicle (UAV) applications has made autonomous identification technology critical for flight safety and collaborative operations. In this paper, we propose and systematically analyze an autonomous identification scheme based on Bluetooth Low Energy (BLE) technology. We formulate a comprehensive system model that integrates link budget, packet collision, identification success probability, and power consumption. By incorporating safety interval constraints and a three-channel integrated reception probability, we employ an exhaustive search algorithm to optimize monitoring strategy parameters, thereby achieving an optimal trade-off between the Recognition Success Rate (RSR) and power consumption. Simulation results indicate that, at a PHY 1 Mbps rate, the optimal monitoring strategy theoretically approaches the Target Level of Safety (TLS) requirements for civil UAVs under the defined model assumptions, with a power consumption of 19.24 mW and an Average First Identification Delay (AFID) of 105 ms. Furthermore, simulation analysis verifies the scheme’s feasibility under dynamic topology, interference, and multi-UAV scenarios, providing a solid theoretical and technical reference for the practical implementation of autonomous UAV identification.

An analysis of air traffic complexity in multi-airport terminal areas can assist air traffic controllers in accurately assessing the air traffic situation and collaboratively managing air traffic flows, thereby enhancing the utilization of airspace resources and reducing flight delays. This paper proposes an air traffic complexity analysis method for multi-airport terminal areas based on a route segment–flight state interdependent network. The interdependent network model consists of an upper-layer flight state network, a lower-layer route segment network, and inter-layer coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network uses route segments as nodes and the connectivity between route segments as edges. The inter-layer coupling edges are determined by evaluating the relationship between aircraft and route segments—if an aircraft is on a specific route segment, a coupling edge exists between the corresponding aircraft node and route segment node. Based on this model, node-level complexity metrics are established to analyze the importance and complexity of individual route segments. Additionally, network-level complexity metrics are introduced to assess the overall air traffic complexity in multi-airport terminal areas. Finally, the method proposed in this paper is validated using flight scenarios in the Guangdong–Hong Kong–Macao Greater Bay Area. By comparing and analyzing the results with the actual situation, it is shown that the proposed method can accurately assess the air traffic complexity in multi-airport terminal areas.

Open-vocabulary semantic segmentation (OVSS) of unmanned aerial vehicle (UAV) imagery aims to recognize arbitrary text-specified categories in aerial scenes, but existing OVSS models often suffer from UAV-domain shifts. To provide a reproducible testbed, this paper constructs a unified UAV OVSS benchmark by reorganizing multiple UAV segmentation datasets into cross-dataset transfer settings with explicit category harmonization and seen/unseen vocabulary analysis. Based on this benchmark, we propose UAV-OVSeg, a Cost Aggregation-style dense matching framework enhanced in two complementary directions: an LLM-guided Category Expansion Module that converts raw category names into structured UAV-aware descriptions, and a DINO-enhanced Geometric Feature Fusion Module that injects local structure into dense visual–text matching. Under SynDrone training, UAV-OVSeg achieves 65.5% mean intersection over union (mIoU) and 78.0% mean accuracy (mACC), improving upon the CAT-Seg baseline by 2.6 mIoU and 3.2 mACC. Under Aeroscapes training, it achieves 64.5% mIoU and 76.8% mACC, improving upon CAT-Seg by 2.6 mIoU and 3.1 mACC. Additional analyses of LLM variants, object scales, prompt sensitivity, boundary quality, and computational cost further verify the effectiveness and reproducibility of the proposed UAV-oriented text–visual enhancement strategy.