基于时间序列测量，重构编码模型变量间影响关系的参数，是复杂网络耦合系统理论中一个尚未解决的重要问题。本文针对一类含噪声的领导者-跟随者共识算法，提出该问题的一种解决方案：仅可获取跟随者节点的测量数据，而无法观测领导者节点。利用此类系统的有向拉普拉斯耦合结构，我们推导出观测动力学的自回归展开式，并可根据领导者的记忆长度在不同阶数处截断。当领导者记忆较短时，在附加若干消除重构退化性的系统假设下，该方法可准确重构包含隐藏领导者智能体的完整动力学矩阵。我们通过数值模拟验证了该理论，分别考察了单个及多个隐藏领导者的情形。

Reconstructing the parameters that encode the influence between model variables based on time-series measurements represents an outstanding question in the theory of complex network-coupled systems. Here, we propose a solution to this problem for a class of noisy leader-follower consensus algorithm, where one has access to measurements only from the followers but not from the leaders. Leveraging the directed Laplacian coupling of such systems, we present an autoregressive expansion of the observed dynamics which can be truncated at different orders, depending on the memory of the leaders. When their memory is short, this allows one to correctly reconstruct the full dynamical matrix with hidden leader agents, provided some additional assumption on the system to lift the degeneracy in the reconstruction. We illustrate and check the theory using numerical simulations for the cases of both a single and multiple hidden leaders.