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.

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR.