基于稀疏重构的KA-STAP杂噪协方差矩阵高精度估计算法
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摘要
针对机载非正侧视阵雷达近程杂波的距离非平稳性,研究了基于稀疏重构的KA-STAP杂噪协方差矩阵高精度估计算法。首先利用稀疏重构获取高分辨率二维空时谱,筛选出符合杂波轨迹分布的像素点,随后利用加权最小二乘法对杂波轨迹进行拟合并估计噪声功率,从而构造先验杂噪协方差矩阵用于STAP权值计算及自适应滤波。仿真实验表明,该算法可有效提升STAP系统在非平稳杂波环境下的杂波抑制与目标检测性能。
Aiming at the range dependence of short-range clutter of non-sidelooking airbone radar,we investigated a high precision estimation method of knowledge-aided space-time adaptive processing(KA-STAP)clutter plus noise covariance matrix based on sparse reconstruction.The method obtains high resolution spatial-temporal spectrum using reduced dimension sparse reconstruction and selects pixels distributed along the clutter ridge,then utilizes weighted least square method to fit the clutter track and estimate the noise power.Subsequently,the prior clutter plus noise covariance matrix can be constructed and applied to STAP weight calculation and adaptive filtering.Simulation results show that the proposed method has advantages in clutter suppression and target detection over conventional STAP algorithm in the nonstationary clutter environment.
Aiming at the range dependence of short-range clutter of non-sidelooking airbone radar,we investigated a high precision estimation method of knowledge-aided space-time adaptive processing(KA-STAP)clutter plus noise covariance matrix based on sparse reconstruction.The method obtains high resolution spatial-temporal spectrum using reduced dimension sparse reconstruction and selects pixels distributed along the clutter ridge,then utilizes weighted least square method to fit the clutter track and estimate the noise power.Subsequently,the prior clutter plus noise covariance matrix can be constructed and applied to STAP weight calculation and adaptive filtering.Simulation results show that the proposed method has advantages in clutter suppression and target detection over conventional STAP algorithm in the nonstationary clutter environment.
引文
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[3]REED I S,MALLET J D,BRENNAN L E.Rapid convergence rate in adaptive arrays[J].IEEE Transactions on Aerospace a nd Electronic Systems,1974,10(6):853-863.
[4]范西昆,曲毅.知识辅助机载雷达杂波抑制方法研究进展[J].电子学报,2012,40(6):1199-1206.
[5]吴亿锋,王彤,吴建新,等.基于道路信息的知识辅助空时自适应处理[J].电子与信息学报,2015,37(3):613-618.
[6]WU Y,WANG T,WU J,et al.Robust training samples selection algorithm based on spectral similarity for space-time adaptive processing in heterogeneous interference environments[J].IET Radar,Sonar&Navigation,2015,9(7):778-782.
[7]CAPRARO C T,CAPRARO G T,BRADARIC I.Implementing digital terrain data in knowledge-aided space-time adaptive processing[J].IEEE Transaction on Aerospace and Electronic Systems,2006,42(3):487-496.
[8]BERGIN J S,TEIXEIRA C M,TECHAU P M,et al.STAP with knowledge-aided data pre-whitening[C].Proceedings of the IEEE Radar Conference,Philadelphia,PA,USA,2004:289-294.
[9]张圣鹋,何子述,李军,等.一种稳健的知识辅助STAP色加载系数优化算法[J].电子与信息学报,2016,38(8):1942-1949.
[10]GAO Z,TAO H.Robust STAP algorithm based on knowledge-aided SR for airborne radar[J].LET Radar Sonar&Navigation,2017,11(2):321-329.
[11]HAN S,FAN C,HUANG X.A novel STAP based on spectrum-aided reduced-dimension clutter sparse recovery[J].IEEE Geoscience&Remote Sensing Letters,2017,14(2):213-217.
[12]KE S,HAO Z,GANG L,et al.A novel STAP algorithm using sparse recovery technique[C].Proceedings of International Conference on Geoscience and Remote Sensing,Cape Town,South Africa,12-17July 2009:336-339.
[13]刘波,刘宝泉,陈春晖.机载预警雷达测高精度分析[J].雷达科学与技术,2012,10(2):133-137.
[14]孙国强,田芳宁.雷达测高误差因素分析与修正[J].国外电子测量技术,2011,30(5):26-28.
[15]GOLDSTEIN J S,REED I S.Subspace selection for partially adaptive sensor array processing[J].IEEETransactions on Aerospace and Electronic Systems,1997,33(2):988-996.
[16]GOLDSTEIN J S,REED I S,SCHARF L A.A multistage representation of the Wiener filter based on orthogonal projections[J].IEEE Transaction on Information Theory,1998,44(7):2943-2959.