信号处理2025，Vol.41Issue(5)：840-852,13.DOI:10.12466/xhcl.2025.05.006
鸟类活跃度量化建模与预测方法研究
Bird Activity Quantification and Prediction Methods
摘要
Abstract
The advancement of low-altitude airspace utilization and the subsequent rise of the low-altitude economy have introduced new challenges and opportunities in airspace safety management.The reliable surveillance of non-cooperative targets,particularly birds,within low-altitude airspace,is a critical prerequisite for ensuring safe and efficient opera-tions of crewed and uncrewed aerial vehicles.Birds are frequently categorized as representative"low,slow,and small"targets and pose significant risks to the safety of low-altitude flight operations owing to their unpredictable movements and potential for causing bird strikes.Bird strikes are a major threat to aviation safety,particularly in areas with dense bird populations or during migration seasons.These incidents can result in significant damage to aircraft,injuries to pas-sengers and crew,and even fatal accidents.Therefore,effective surveillance of birds in low-altitude airspace is impera-tive to mitigate the risk of bird strikes and ensure the safe operation of low-altitude flights.Hence,professional bird-detection radar systems have been developed to provide comprehensive and accurate information on bird targets.These systems offer all-weather,wide-area surveillance capabilities,capturing rich samples of bird activity that can be ana-lyzed to uncover patterns and trends.By understanding the behavior and activity patterns of birds,we can better predict their movements and take proactive measures to avoid potential collisions.Furthermore,the relationship between bird activity patterns and meteorological factors is well-documented.Variations in weather conditions,such as temperature,humidity,wind speed,and wind direction can significantly influence bird behavior.By incorporating meteorological data into bird activity prediction models,we can further improve the accuracy and reliability of these models.This pro-vides valuable insights for airport wildlife management and efforts to prevent bird strikes.In this paper,we propose a comprehensive model for quantifying bird activity based on professional bird-detection radar observation data and pre-dicting bird activity based on meteorological parameters.The model consists of two main components:a calculation model for bird activity intensity and a prediction model based on multiple meteorological parameters.The calculation model utilizes the radar dataset to assess the intensity of bird activity in both spatial and temporal domains,whereas the prediction model employs random forest algorithms to establish relationships between bird activity intensity levels and meteorological parameters.Experimental results demonstrate the feasibility of predicting bird activity intensity levels based on multiple meteorological parameters.By refining the model and incorporating additional data sources,such as high-resolution meteorological data and long-term bird activity records,we expect to further improve the prediction ac-curacy.This can support more effective and efficient bird strike prevention measures,ensuring the safe and orderly de-velopment of the low-altitude economy.In conclusion,the reliable surveillance of non-cooperative targets,particularly birds,within low-altitude airspace is crucial for the safe and efficient operation of low-altitude flights.By leveraging pro-fessional bird-detection radar systems and incorporating meteorological data into bird activity prediction models,we can significantly enhance our ability to predict and mitigate the risks associated with bird strikes.As research continues to ad-vance,we anticipate the development of even more sophisticated and effective surveillance and prediction systems that can support the sustainable growth of the low-altitude economy.关键词
低慢小目标/雷达探测/雷达数据挖掘/鸟击防范Key words
low slow small targets/radar detection/radar data mining/bird strike avoidance分类
信息技术与安全科学引用本文复制引用
刘佳,陈唯实,陈小龙,赵志坚..鸟类活跃度量化建模与预测方法研究[J].信号处理,2025,41(5):840-852,13.基金项目
国家自然科学基金面上项目(62371018) （62371018）
国家自然科学基金委员会-中国民用航空局民航联合研究基金(U2433211) （U2433211）
国家重点研发计划(2024YFB3909800) （2024YFB3909800）
国家自然科学基金(62222120) （62222120）
山东省自然科学基金(ZR2024JQ003) The National Natural Science Foundation of China(62371018) （ZR2024JQ003）
National Natural Science Foundation of China(NSFC)and Civil Aviation Administration of China(CAAC)(U2433211) （NSFC）
National Key Research and Development Program of China(2024YFB3909800) （2024YFB3909800）
The National Natural Science Foundation of China(62222120) （62222120）
Shandong Provincial Natural Science Foundation(ZR2024JQ003) （ZR2024JQ003）
