Control Requirements for Robust Beamforming in Multi-Satellite Systems

TL;DR

The paper addresses the vulnerability of coherent beamforming in multi-satellite formations to position and attitude perturbations. It adopts a control-aware approach, integrating attitude and orbit information into MRT-based precoding to compensate geometric phase errors, demonstrating that calibrated alignment can preserve nominal beam patterns despite perturbations. Key findings show that without calibration, small translations along the propagation axis and modest rotations can severely degrade the main lobe, HPBW, and SLL, but incorporating perturbation data restores performance and can reduce maneuver requirements. The results support deploying onboard sensing and closed-loop beamforming to enable robust, autonomous multi-satellite coordination in next-generation systems, with practical implications for real-time communication and sensing at LEO scales.

Abstract

This work investigates the impact of position and attitude perturbations on the beamforming performance of multi-satellite systems. The system under analysis is a formation of small satellites equipped with direct radiating arrays that synthesise a large virtual antenna aperture. The results show that performance is highly sensitive to the considered perturbations. However, by incorporating position and attitude information into the beamforming process, nominal performance can be effectively restored. These findings support the development of control-aware beamforming strategies that tightly integrate the attitude and orbit control system with signal processing to enable robust beamforming and autonomous coordination.

Figures (3)

• Figure 1: Simplified representation of the system model with two satellites and the beam boresight direction. Satellite $n = 0$ defines the reference frame, while satellite $n = 1$ is subject to a pitch rotation.
• Figure 2: Representative simulation with $N_s = 64$ satellites under translation and rotation perturbations. \ref{['fig:singlerealization_patterncutComparison']}$\theta$-cut at $\varphi = 0$ comparing nominal, perturbed, and calibrated patterns. \ref{['fig:singlerealization_uvPerturbed']}$uv$ power pattern for the perturbed configuration. \ref{['fig:singlerealization_uvCalibrated']}$uv$ power pattern after phase-aware calibration.
• Figure 3: Monte Carlo simulation results under attitude and position perturbations. The first row (\ref{['fig:montecarlo_11_deltaG_drot']}–\ref{['fig:montecarlo_13_deltaGsll_drot']}) shows performance degradation due to attitude perturbations. The second row (\ref{['fig:montecarlo_21_deltaG_dtrans']}–\ref{['fig:montecarlo_23_deltaGsll_dtrans']}) shows the impact of position perturbations.