arXiv:2611.06719v1 Announce Type: new Abstract: Cooperation among low-Earth-orbit (LEO) satellites provides spatial diversity for mitigating uplink interference that is difficult to suppress with a single onboard array. Coherent interference cancellation across satellites, however, requires compensation of the relative delay and Doppler of a common interferer. We analyze two-branch interference nulling with finite training and derive a closed-form approximation to the mean residual interference-to-noise ratio (INR). The approximation separates wrong acquisition, local synchronization errors, and weight-learning losses, linking performance to input INR, branch power imbalance, training length, and effective search size. Under a Gaussian surrogate with small errors and efficient, approximately unbiased refinement, correct acquisition yields a high-INR plateau of $2/N_v$, where $N_v$ is the valid training length. Synchronization and weight learning contribute equally to this limit, whereas persistent alignment bias adds a penalty that grows with input power. Monte Carlo experiments compare dense FFT/zoom and coarse FFT/gradient acquisition using matched final FIR refinement, branch-noise normalization, and independent test blocks. Both approach the predicted plateau at high input INR. Coarse search reduces measured search time by $6.7\times$ at an approximately 0.7-dB acquisition-threshold penalty. Controlled power-ratio sweeps show how branch imbalance delays reliable acquisition at fixed stronger-branch INR. A separate Starlink snapshot evaluation demonstrates comparable nulling performance under changing satellite geometry.