Robust UAV Beamforming Under Pointing Jitter Using Self-Centered Covariance Reconstruction

UAV-mounted arrays are highly sensitive to pointing/attitude jitter, which induces steering-vector mismatch and can severely degrade minimum-variance beamformers under strong interference and limited snapshots. This letter presents a robust design that couples self-centered interference-plus-noise covariance matrix (INCM) reconstruction with derivative-constrained minimum variance distortionless response (MVDR) beamforming. A short local Capon scan provides a data-driven drift-aligned direction, and the INCM is reconstructed by integrating a regularized, noise-floor-subtracted Capon spectrum over the field of view while excluding a neighborhood around the self-centered direction to suppress desired-signal leakage. Based on the reconstructed INCM, we solve a derivative-constrained MVDR problem with a white-noise-gain (WNG) bound to limit mainlobe variation within the jitter neighborhood and prevent excessive noise amplification. The resulting design is formulated as a convex second-order cone program (SOCP). Simulations show consistent output-SINR gains over worst-case robust MVDR and representative covariance-reconstruction baselines across SNR sweeps, increasing jitter levels, snapshot-limited regimes, and varying interference conditions.