综合孔径微波辐射计的射频干扰源空间角度稀疏贝叶斯估计方法

A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer

该文提出一种综合孔径微波辐射计射频干扰源(RFI)空间稀疏贝叶斯估计方法。首先建立了综合孔径微波辐射计可见度函数干涉测量模型,观测数据表示为综合孔径天线基线对相关导向矢量观测矩阵与视场亮温的乘积,由于相关导向矢量观测矩阵的正交性和RFI空间角度分布的稀疏性,亮温在基线对相关导向矢量观测矩阵正交基所构成的支撑域中的变换系数是稀疏的。该文在稀疏贝叶斯学习(SBL)框架下对亮温进行稀疏重构。该方法在无需稀疏度和正则化参数等先验信息前提下也能获得较高的重构性能。计算机仿真验证了该方法的有效性。

A sparse Bayesian estimation for spatial Radio Frequency Interference(RFI)of synthetic aperture microwave radiometers is proposed in this paper.Firstly,an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established.The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view.Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution,the transformation coefficients of brightness temperature in the support domain are sparse.Under the Sparse Bayesian Learning(SBL)framework,brightness temperature is sparsely reconstructed.Notably,this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters.The effectiveness of this method is verified through computer simulations.