A Cascaded Autofocus Method for Earth-Based Radar Moon 3-D Imaging under Multi-Source Phase Errors based on Entropy Gradient

The Moon is Earth’s only natural satellite, and observation for the Moon is of great importance in scientific research and engineering application. Earth-based radar is a powerful approach for Moon observation, and is capable of acquiring Moon three-dimensional (3-D) radar image. However, various non-ideal factors lead to intra-track and inter-track phase error, which degrade 3-D imaging quality. To address this issue, this manuscript proposes a novel cascaded autofocus method for earth-based radar Moon 3-D imaging. The contribution of the manuscript mainly consists of two parts. The first is the comprehensive modeling for complex phase errors, where different types of phase errors are modeled as polynomial or periodic function of azimuth time. The impact of complex phase errors on Moon 3-D imaging is analyzed. The second is the error estimation and compensation algorithm for multi-source complex phase errors that does not rely on strong scatters. This algorithm uses the image energy maximization criterion to truncate the optimal imaging region for autofocus. The analytical iteration format based on the partial derivation of distributed target entropy is given. In addition, the 2-D and 3-D autofocus is performed in cascade to separately estimate errors in the time-domain, which does not rely on certain coordinate system property. Computer simulations and real data experiments based on earth-based radar system are conducted to validate the proposed method.