CAJ | 학술논문

针对机载单通道雷达前视分辨率不高的问题，该文提出一种基于多帧数据联合处理的贝叶斯前视成像方法。该文首先建立高斯背景下的前视回波信号模型，然后将散射场景的处理空间由单帧波束域的低维空间扩展到多帧波束域联合而成的高维空间以增加其空域稀疏性，并对散射场景的稀疏性进行统计建模。最后基于贝叶斯理论，将前视条件下的雷达成像转化为贝叶斯准则下的优化问题，并通过共轭梯度算法进行优化求解。在优化求解时，稀疏统计参数从数据迭代过程中估计得到。仿真结果和实测数据表明该方法不仅可以对前视场景进行高分辨成像，还可以抑制虚假散射点。
침대궤재단통도뢰체전시분변솔불고적문제，해문제출일충기우다정수거연합처리적패협사전시성상방법。해문수선건립고사배경하적전시회파신호모형，연후장산사장경적처리공간유단정파속역적저유공간확전도다정파속역연합이성적고유공간이증가기공역희소성，병대산사장경적희소성진행통계건모。최후기우패협사이론，장전시조건하적뢰체성상전화위패협사준칙하적우화문제，병통과공액제도산법진행우화구해。재우화구해시，희소통계삼수종수거질대과정중고계득도。방진결과화실측수거표명해방법불부가이대전시장경진행고분변성상，환가이억제허가산사점。
An adaptive Bayesian super-resolution imaging algorithm based on the combined multiple frames data is proposed to enhance the azimuth resolution of airborne single-channel forward-looking radar. The echo of the forward-looking radar in the Gaussian noise is modeled, and the processing space is expanded from the low dimension of single frame data to the high dimension of multiple frames data to enhance the sparsity of domain scatterers. During the framework, the sparsity of the scatterers is modeled in spatial domain, and the imaging is converted into a problem of signal optimization based on Bayesian formalism. The final optimal result can be solved by the conjugate gradient method. In this framework, the statistic parameter is estimated with data-driven. Simulation results demonstrate that the proposed algorithm both can increase the resolution of the forward-looking imaging results and suppress the artifacts.