Satellite networks with dense low Earth orbit (LEO) constellations rely on aggressive spectrum reuse, making co-channel interference a dominant and rapidly varying factor that limits link availability and complicates spectrum sharing and compliance. Satellite radio map (RM) construction is therefore essential for interference cognition, yet it is challenging because the active satellite set is unknown, beam footprints and pointing are not directly observable, and received signal strength (RSS) measurements are difficult to calibrate under coupled link budget variations and noise. These latent uncertainties yield a severely underdetermined inverse problem with strong signature coherence, where existing methods often trade detection recall for precision and still fail to recover a faithful continuous RSS field. This paper proposes a beam-aware RM estimation framework that unifies active satellite identification and RSS field reconstruction through physics-consistent parametric modeling. An interpretable structural prior links geometry and beam shaping to spatial RSS formation, and an adaptive model order selection strategy infers the number of active satellites from measurements by balancing fit and complexity. Extensive experiments across varying signal to noise ratio (SNR), total satellite count, and active satellite count demonstrate consistently higher RSS spatial correlation, lower root mean squared error (RMSE), and improved F1 score, validating the proposed approach for interference-aware satellite RM construction in satellite networks.

翻译：密集低地球轨道（LEO）星座的卫星网络依赖激进的频谱复用，导致同信道干扰成为限制链路可用性并复杂化频谱共享与合规性的主导且快速变化因素。因此，卫星无线地图（RM）构建对于干扰认知至关重要，但由于活跃卫星集未知、波束覆盖与指向不可直接观测，且接收信号强度（RSS）测量在耦合链路预算变化与噪声下难以校准，该问题极具挑战性。这些潜在不确定性导致具有强特征一致性的严重欠定逆问题，现有方法常以检测召回率换取精度，且仍无法恢复可靠的连续RSS场。本文提出一种波束感知的RM估计框架，通过物理一致参数化建模实现活跃卫星识别与RSS场重建的统一。可解释的结构先验将几何与波束赋形关联至空间RSS形成，自适应模型阶数选择策略通过平衡拟合度与复杂度从测量中推断活跃卫星数量。在信噪比（SNR）、总卫星数和活跃卫星数变化条件下的广泛实验表明，该方法持续获得更高的RSS空间相关性、更低的均方根误差（RMSE）及改进的F1分数，验证了所提方法在卫星网络中实现干扰感知RM构建的有效性。