基于拉普拉斯算子的综合孔径辐射计成像算法

Imaging Algorithm of Synthetic Aperture Interferometric Radiometers Based on Laplace Operator

成像反演是综合孔径辐射计的一项关键内容.然而,综合孔径辐射计成像反演是病态的反问题.虽然传统的正则化方法能有效克服其病态性,但依然存在较大的重构误差.为了进一步降低重构误差,提出一种基于拉普拉斯算子的综合孔径辐射计成像算法.与传统的单参数正则化不同,该算法通过引入拉普拉斯算子项,对差分亮温构造拉普拉斯混合正则化,并利用多维扩展的广义交叉验证准则选取2个正则化参数.基于L波段的FPIR样机进行了仿真分析,仿真结果表明:与改进的最小范数正则化和带限正则化相比,该算法均方根误差平均降低了30%,峰值信噪比平均提高了3 dB,证明了其有效性.

The imaging inversion is a key content of synthetic aperture interferometric radiometers(SAIRs). However, the imaging inversion in SAIRs is an ill-posed inverse problem. Although the traditional regularization methods can effectively overcome the ill-conditioned property, there is still a large reconstruction error. In order to further reduce the reconstruction error, an imaging algorithm of SAIRs based on Laplace operator is proposed. Different from traditional single-parameter regularization, the proposed algorithm constructs the Laplace hybrid regularization for differential brightness temperature by introducing a new term of Laplace operator. Moreover, two regularization parameters are selected by use of multi-dimensional extended generalized cross-validation criterion. The simulation is based on the prototype of full polarization interferometric radiometer(FPIR). The simulation results show that compared with improved minimum norm regularization and band-limited regularization, the root mean square error for the proposed algorithm has reduced by30%, and the peak signal to noise ratio has increased by3dB, proving its effectiveness.