摘要
地空探测系统在防空预警、空域监视、战场感知等领域发挥着至关重要的作用,但复杂背景下多源干扰、环境杂波和系统误差导致虚警率居高不下,严重制约其作战效能与可靠性。该文围绕降低虚警率的核心问题,系统分析虚警产生的多元机理,提出基于自适应滤波、多特征融合与阈值动态调整的信号处理方法,研究多传感器信息融合算法,包括数据级、特征级及决策级多层次融合,结合深度学习技术构建智能识别与虚警抑制模型,以期为地空探测系统的智能化和高可靠性应用提供技术参考。
Ground-to-air detection systems play a vital role in the fields of air defense early warning,airspace surveillance,battlefield sensing,etc.However,multi-source interference,environmental clutter and system errors in complex backgrounds lead to high false alarm rates,which seriously restricts their combat effectiveness and reliability.Focusing on the core issue of reducing the false alarm rate,this paper systematically analyzes the multiple mechanisms of false alarm generation,proposes a signal processing method based on adaptive filtering,multiple feature fusion and dynamic threshold adjustment,and studies multisensor information fusion algorithms,including multi-level fusion at the data level,feature level and decision-level,and combines deep learning technology to build an intelligent identification and false alarm suppression model,in order to provide technical reference for intelligent and high-reliability applications of ground-to-air detection systems.
出处
《科技创新与应用》
2026年第4期23-26,共4页
Technology Innovation and Application
关键词
地空探测
虚警率
多传感器融合
深度学习
信号处理
ground-air detection
false alarm rate
multi-sensor fusion
deep learning
signal processing
