信号处理2025，Vol.41Issue(11)：1814-1825,12.DOI:10.12466/xhcl.2025.11.007
基于ISSA-BPDN的机坪杂波背景下鸟类目标微多普勒分量分离方法
Micro-Doppler Separation Method for Bird Targets in Airport Apron Clutter Environment Based on ISSA-BPDN
摘要
Abstract
Bird strikes constitute the primary threat to aviation safety.These incidents predominantly occur during aircraft takeoff and landing phases.Within the airport apron environment,the strong radar echoes generated by large targets such as civil aviation aircraft can overwhelm the faint echoes from bird targets.Consequently,detect-ing bird targets against airport apron clutter is critically important.The micro-Doppler signatures generated by the wing-flapping motion of bird targets contain critical physical information that serves as a valuable basis for the iden-tification and classification of bird targets.However,under strong airport apron clutter conditions,these compo-nents cannot easily be directly extracted.Therefore,the micro-Doppler components arising from wing-flapping echoes within the received radar signals should be separated.To address the challenge of separating micro-Doppler components of bird targets under the background of airport apron clutter,this study proposes a separation method for bird wing-flapping echoes using basis pursuit denoising(BPDN)optimized by an improved sparrow search al-gorithm(ISSA).This method first improves the sparrow search algorithm(SSA)by integrating the Circle chaotic mapping,osprey optimization algorithm(OOA),and Cauchy variation strategy.The Circle chaotic sequence is used to initialize the sparrow population,enhancing the diversity of populations.To enhance the global optimiza-tion capability,the OOA is employed to modify the explorer update formula.The Cauchy variation is used to per-turb the follower positions,enhancing the algorithm's ability to escape local optima.Subsequently,to mitigate the degradation of micro-Doppler signal separation performance caused by manual parameter selection in BPDN,the ISSA is employed to optimize the regularization parameter and the augmented Lagrangian parameter applied in BPDN.This optimization strategy mitigates the influence of manually configured parameters on algorithm perfor-mance,thereby enabling the determination of critical parameters.Finally,based on the optimal parameter combina-tion,the radar echo signals from bird targets are reconstructed using the different sparse characteristics of multi-component signals in various transform domains,achieving the separation of airport apron clutter components,bird body components,and micro-Doppler components from bird echoes.Experimental results from both simulation and measured data demonstrate that the ISSA exhibits higher convergence speed and accuracy than conventional optimi-zation algorithms(e.g.,particle swarm optimization).The BPDN with optimized parameters effectively separates the micro-Doppler components of bird echoes,providing a prerequisite and theoretical foundation for the subse-quent bird target parameter estimation.关键词
鸟类目标/机坪杂波/微多普勒/信号分离/改进麻雀搜索算法Key words
bird targets/airport apron clutter/micro-Doppler/signal separation/improved sparrow search algorithm分类
信息技术与安全科学引用本文复制引用
何炜琨,郭宏伟,尚肖霄..基于ISSA-BPDN的机坪杂波背景下鸟类目标微多普勒分量分离方法[J].信号处理,2025,41(11):1814-1825,12.基金项目
天津市教委科研计划重点项目(2022ZD005)Key Project of Tianjin Education Commission Science and Technology plan(2022ZD005) （2022ZD005）
