短相干处理间隔条件下机载监视雷达风电场杂波抑制方法

Method to Suppress Wind Farm Clutter for Short CPI Airborne Surveillance Radars

风能作为一种清洁能源已受到各国关注。风电场的存在除对地基雷达产生影响外,还可能对机载监视雷达等工作性能产生影响。短相干处理间隔(Coherent Processing Interval,CPI)导致的机载监视雷达回波信号谱分辨率降低及风电场杂波微动特征不明显等问题,会影响杂波抑制性能,考虑到载机平台运动对雷达回波频谱的调制作用及地杂波的影响,利用瞬态雷达回波在频域的稀疏特性,提出基于增广拉格朗日优化及分裂变量(Augmented Lagrangian Method and Variable Splitting,ALM-VS)的机载雷达风电场回波特征恢复方法,首先对短CPI机载监视雷达回波信号滑窗,得到多个雷达回波信号分量,其次利用分裂增广拉格朗日方法迭代求解各分量的最优表示系数,恢复各个信号分量,利用恢复后的各个信号分量逆滑窗重构完整数据,以此提升短CPI机载监视雷达风电场雷达回波频谱分辨率及微动特征,在此基础上进一步利用形态成分分析(Morphological Component Analysis,MCA)和低秩矩阵优化(Low-Rank Matrix Optimization,LRMO)相结合的方法实现短CPI机载监视雷达非平稳动态风电场杂波的抑制。实验结果表明,ALM-VS特征恢复方法在目标与杂波处于相同距离单元和不同距离单元情况下均可提升短CPI机载监视雷达回波信号谱分辨率,增强其微动特征,实现风电场杂波抑制。在相同条件下,与迭代自适应(Iterative Adaptive Approach,IAA)特征恢复方法相比,所提的ALM-VS特征恢复方法的运算效率提升85.6%。

Wind power,a clean energy,has gained interest worldwide.The performance of airborne surveillance radars may be affected by the reflected echoes of wind farms.Short coherent processing intervals(CPIs)cause problems such as a reduced spectral resolution of the radar echoes and unclear fretting characteristics of wind farms,and the performance of the clutter suppression method is affected.In this study,the effect of modulation on the spectrum of radar echoes and the influence of ground clutter are considered,and the sparse characteristics of transient radar echoes in the frequency domain are used.A feature recovery method of wind farm clutter for airborne radar,based on Augmented Lagrangian Method and Variable Splitting(ALM-VS),is proposed.First,multiple signal components are obtained by sliding the window.Second,the split-augmented Lagrangian method is used to iteratively solve the optimal sparse representation coefficients of each component,and each signal component can be recovered.The complete data can be intensively reconstructed by using the inverse sliding window to improve the spectral resolution and micro-motion characteristics of the wind farm radar echoes.Based on this,a combined method of morphological component analysis(MCA)and low rank matrix optimization(LRMO)is used to suppress non-stationary dynamic wind farm clutter for the short CPI airborne surveillance radar.The experimental results show that the feature recovery method based on ALM-VS can improve the spectral resolution of the radar echoes for short CPI airborne surveillance and subsequently enhance its micro-motion characteristics.Wind farm clutter can be suppressed if the target and clutter are either in the same or different distance units.Under the same conditions,the efficiency of the proposed ALM-VS method can be increased by 85.6%compared with the Iterative Adaptive Approach(IAA)method.