Sub-Aperture Averaged Minimum Norm Eigen-Canceler for Airborne Radar Clutter Suppression

Airborne radars are essential tools in geoscience remote sensing, where robust clutter suppression is critical for extracting weak geophysical signals and target returns. In practice, complex terrain frequently induces highly non-homogeneous clutter, which severely limits the independent and identically distributed (i.i.d.) training samples available for clutter covariance matrix (CCM) estimation, thereby degrading the performance of space-time adaptive processing (STAP). This letter proposes a Sub-aperture Averaged Minimum Norm Eigen-Canceler (SA-MNEC) to enhance clutter suppression under sample-limited conditions. An improved sub-aperture averaging scheme is developed to achieve robust sub-aperture CCM estimation by mitigating the influence of inter-channel amplitude-phase errors. The averaged CCM is then integrated into an MNEC framework to exploit the low-rank clutter structure, ensuring effective clutter rejection while minimizing performance loss caused by insufficient training data. Simulation and experimental results using real airborne radar data demonstrate the effectiveness of the proposed algorithm.