A collision avoidance method that is specifically tailored for UAVs (unmanned aerial vehicles) operating in converging airspace is proposed. The method is based on ADS-B messages and it aims to detect and resolve conflicts between UAVs. The proposed method involves two main steps. First, a UAV conflict-sensing scheme is developed, which utilizes ADS-B information flow path and analyzes the message format information. Second, an unscented Kalman filter is used to predict UAV trajectories based on the acquired ADS-B information. The predicted information is then used to determine potential conflict scenarios, and different deconfliction strategies are selected accordingly. These strategies include speed regulation, direction regulation, and compound deconfliction, and are mathematically validated using the velocity obstacle method. The feasibility and effectiveness of the proposed method are evaluated through simulation, and it is concluded that the method can significantly improve the conflict resolution capability of UAV flights. This research provides a valuable contribution to the field of UAV collision avoidance, and can serve as a theoretical foundation for further advancements in this area.

Path planning technology is the precondition of unmanned aerial vehicle (UAV) airspace safety. For in the running features of the UAV in 3-D space environment, putting forward using fight level (FL) guiding factor to improve the basic ant colony optimization(ACO), which improved the quality and the operation efficiency of path planning; In view of the ACO which easily fell into local optimum in huge space, putting forward using split method to make the improved ACO have a better effect in huge path planning problem; Through simulation experiments, the basic algorithm and improved algorithm and split method were analyzed, the results show that the improved algorithm has a good 3-D path planning ability.

To address controlled flight into terrain (CFIT) accidents, the Automatic Ground Collision Avoidance System (Auto-GCAS) was developed and implemented. This paper focuses on the trajectory prediction module within the Auto-GCAS. Aiming to overcome the limitations of traditional trajectory prediction algorithms, such as insufficient prediction accuracy and inadequate capability to capture features of nonlinear complex motion trajectories, a trajectory prediction method based on CPO-optimized CNN-LSTM-Attention is proposed. The Crested Porcupine Optimization (CPO) algorithm is employed to optimize the parameters of the CNN-LSTM-Attention network, primarily to determine the optimal learning rate and the optimal number of hidden nodes. Both qualitative and quantitative experimental approaches were adopted, using function simulations to model the complex motion trajectories of fighter aircraft. Comparative experiments were conducted to evaluate the prediction performance of four models: LSTM, CNN-LSTM, CNN-LSTM-Attention, and CPO-CNN-LSTM-Attention. The experimental results demonstrate that the CPO-CLA model exhibits significant advantages in model convergence speed and trajectory prediction accuracy, with notable improvements over the traditional LSTM model in terms of RMSE, MAE, and MAPE metrics.

Addressing the challenge of identifying pivotal segments within the aviation network, exacer-bated by the escalating air traffic and airspace congestion, this study employs the FP-Growth algorithm to enhance the correlation analysis of the segment network's edges. This enhancement facilitates the construction and detailed examination of the segment network's correlation model. The methodology commences with the development of a segment correlation network model, leveraging relative hourly flight traffic data. Subsequently, a thorough analysis of the model network's topological characteristics is conducted. The findings reveal that the refined FP-Growth algorithm outperforms both the standard FP-Growth and Apriori algorithms in terms of efficiency and accuracy. Moreover, the application of degree centrality, pagerank, and eigenvector centrality successfully identifies influential segments, such as ENH-P373 and YIH-ENH, which play a crucial role in the operational efficiency and overall safety of the aviation network. This research provides a solid theoretical foundation for the precise identification and management of critical network segments.