基于多帧数据联合处理的机载单通道雷达贝叶斯前视成像

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。

Bayesian Forward-looking Imaging for Airborne Single-channel Radar Based on Combined Multiple Frames Data

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.