Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model.

Swarm formulation is a new UAV (Unmanned Aerial Vehicle) derivation with high application significances and military threats. UAV swarms are nonrigid deformable extended radar targets. Their multimodality characters bring higher Radar Cross Section (RCS) variety and statistical uncertainty. Conventional full-wave electromagnetic solvers are problematic in resource consumptions for swarm target RCS modellings and simulations. This paper introduces an efficient UAV swarm RCS restoration framework by integrating EPA (Equivalent Principle Algorithm) and EIM (Empirical Interpolation Method) with particular customization strategies. Each UAV within a swarm is enclosed by an equivalent surface to reduce meshing complexity and maintain unknown consistency. Interpolation points and basis functions are extracted from training spaces to restore electric and magnetic surface currents for arbitrary swarm modalities efficiently. New training parameter selection and solution approximation methods are developed to accommodate swarm target scattering problems. Quadcopter drone and fixed wing UAV swarm models are developed for verification. The proposed method is validated and analyzed from multiple aspects. RCS results present good numerical consistency with benchmark results from simulation and measurement solutions. Its efficiency superiority is distinctive in both memory and time consumptions compared with numerical solvers. This enables the RCS study for arbitrary dynamic UAV swarm configurations. Numerical performance dependence on UAV swarm configurations is studied to explore its applicability conditions. Training parameter optimization and EIM strategy modification are promising to elevate RCS restoration accuracy and efficiency performance of the proposed framework.

Radar target range profile possesses particular advantages in many target recognition problems. Feature extraction techniques could optimize target recognition performance but conventional methods neglect specific characters of electromagnetic scattering mechanism in range profiles. This paper introduces a range profile segmentation algorithm which decompose target range profile into segments corresponding to particular electromagnetic scattering mechanisms. Segment type identification and weighing algorithms are also developed for distributed classification strategy. Results indicate that the proposed method could achieve comparable classification accuracy as advanced neural networks but requires much less computational resources to guarantee its application flexibility.

Birds in airport airspaces are critical threats to aviation safety. Avian radar systems are effective for long-range bird monitoring and hazard warning, but their functionalities are confined to a short-term temporal scale. Spatial–temporal activity modeling and characterization for birds are not studied comprehensively from historical radar datasets. This paper proposes a radar data analysis framework to characterize bird activities as a long-term functionality complement. Spatial domain modeling initializes data mining by extracting reference spots for data filtering. Bird activities are quantified in the temporal domain. Activity degrees are utilized for periodicity extraction with the daily segment random permutation strategy. Categorical probabilities are calculated to interpret bird activity periodicity characters. Historical radar datasets collected from an avian radar system are adopted for validation. The extracted activity periodicity trends for diurnal birds present prominent consistency with artificial observation records. Migratory bird periodicity trends present a good match with ornithology understandings. A preliminary experiment is presented to indicate the possibility of predicting bird activity levels, especially for migratory birds.