Accurate detection and tracking of birds and drones are of great significance in various low altitude airspace surveillance scenarios. Radar is currently the most proper long range surveillance technology for this problem but also challenged by various difficulties on effective distinguishing between birds and drones. This paper explores the inherent flight mechanic and behavior mode of birds and drones. A target classification method is proposed by extracting target motion characteristics from radar tracks. The random forest model is selected for target classification in the new feature space. The proposed method is verified by real bird surveillance radar systems deployed in airport region. Classification results on birds, quadcopter drones and dynamic precipitations indicate that the proposed method could provide good classification accuracy. The Gini importance descriptors in random forest model provide extra reference on motion characteristic evaluation and mining. High sample flexibility and efficiency make the classification system capable of handling complicated low altitude target surveillance and classification problems. Limitations of the existing method and potential optimization strategy are also discussed as future works.

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works.

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.

The Automatic Target Recognition (ATR) with target High-Resolution Range Profile (HRRP) is of great research and application significance. Environmental interferences reflect close relevance with target HRRP imaging quality as well as classification performance. The dynamic target attitude perturbation is considered as one of the dominant interference factors. This paper studies its impact on aircraft HRRP through simulation and statistical analysis. HRRP confidence zone and similarity concepts are proposed as qualitative and quantitative tools for HRRP variation description under attitude perturbation. Extracted HRRP variation features provide a novel solution to overcome attitude perturbation interference in target classification while maintaining the accuracy. Classification evaluations over three aircraft models in accuracy and efficiency consolidate the proposed classification method which better suits the practical classification scenario.

The mode matching method is an efficient solution to conventional microwave waveguide problems for its dimensionality advantages. It is theoretically extendable to computational lithography problems in nano scales with periodic boundary condition. The high computational complexity of conventional mode matching limits its application significance in highly complicated nano scale problems. This paper introduces an optimized efficient mode matching method for computational lithography problems whose complexity is O( $\text{N}^{1.5}$ ). This bottleneck is breached by transforming governing equations with Lanczos algorithm. A novel hybrid eigenmode restoration algorithm is proposed to solve the eigenmode accuracy loss by involving the Arnoldi method. Benchmark cases verify the accuracy of the restored eigenmode and mode matching method with from microwave to nano scale problems. The efficiency is further optimized by exploring the relevance between iteration step and structure complexity. The non-uniform iteration step is introduced for efficiency optimization. A real mask component is modelled as a multi-junctional waveguide structure with periodic boundary condition. Simulation results validate the effectiveness of the proposed method and efficiency benefits are discussed through comparison with other solvers including HFSS and RCWA. Results indicate that the proposed method possesses good flexibility and application significance for computational lithography problems with high complexity.

Avian radar systems are effective for wide-area bird detection and tracking, but application significances need further exploration. Existing radar data mining methods provide long-term functionalities, but they are problematic for bird activity modelling especially in temporal domain. This paper complements this insufficiency by introducing a temporal bird activity extraction and interpretation method. The bird behaviour is quantified as the activity degree which integrates intensity and uncertainty characters with an entropy weighing algorithm. The method is applicable in multiple temporal scales. Historical radar dataset from a system deployed in an airport is adopted for verification. Temporal characters demonstrate good consistency with understandings from local observers and ornithologists. Daily commuting and roosting characters of local birds are well reflected, evening bat activities are also extracted. Night migration activities are demonstrated clearly. Results indicate the proposed method is effective in temporal bird activity modelling and interpretation. Its integration with bird strike risk models might be more useful for airport safety management with wildlife interference.