AI 中文总结
针对在轨稀疏测量下卫星天线方向图重建难题，提出协作式框架，采用截断DCT基构建低维模型，结合中点均匀采样与D最优采样，经仿真验证了方法的准确性与样本效率及重建增益。
AI 中文摘要
在轨精确重建卫星发射天线方向图存在困难，因为正常任务运行期间仅能获得稀疏的定向测量数据。本文提出一种协作式在轨方向图重建框架，该框架将接收的校准功率转换为归一化定向样本，并使用截断离散余弦变换（DCT）基表示天线功率方向图。所得低维模型将高维方向图恢复转化为系数估计问题，针对该问题推导了闭式最大似然估计器与误差表征。分析表明，DCT截断误差、测量噪声及采样基条件数会共同影响重建精度。对于可定期访问的角扇区，中点均匀采样为保留的DCT模式提供了信息均衡的基线；对于受约束的可行机会，采用D最优采样选择信息丰富的测量方向。仿真验证了角离散化的准确性、截断DCT模型的样本效率，以及D最优采样在轨道不规则产生的可行机会下的重建增益。
英文摘要
Accurately reconstructing satellite transmit-antenna patterns on orbit is difficult because only sparse directional measurements are available during normal mission operations. This paper develops a cooperative on-orbit pattern-reconstruction framework that converts received calibration power into normalized directional samples and represents the antenna power pattern using a truncated discrete cosine transform (DCT) basis. The resulting low-dimensional model transforms high-dimensional pattern recovery into coefficient estimation, for which a closed-form maximum-likelihood estimator and error characterization are derived. The analysis shows how DCT truncation error, measurement noise, and sampled-basis conditioning jointly affect reconstruction accuracy. For regularly accessible angular sectors, midpoint-uniform sampling provides an information-balanced baseline for the retained DCT modes. For constrained feasible opportunities, D-optimal sampling is used to select informative measurement directions. Simulations verify the accuracy of the angular discretization, the sample efficiency of the truncated-DCT model, and the reconstruction gain of D-optimal sampling under irregular orbit-generated opportunities.
Comments12 pages, 9 figures. Submitted for possible publication
