针对分布式协同被动定位跟踪中多源异构传感器估计值不一致的问题，提出一种基于快速协方差交互(FCI)的分布式一致性算法。首先，基于泰勒公式和密度聚类理论构建多传感器时间配准模型，以解决多源异步采样带来的时间不同步问题，随后各个传感器结合邻居的信息贡献完成局部估计。然后，通过给定的一致性稳态误差，理论推导迭代次数与一致性误差之间的关系，从而确保信息组在有限迭代次数内收敛到一致。在此基础上，每个传感器仅需进行一次全局的快速协方差交互即可完成信息融合，显著提升融合的效率和精度。最后，将其应用到多源分布式协同被动定位系统，仿真结果表明所提出的算法可以在完成时间配准同时，能够在有限迭代次数内有效完成一致性融合定位。

To address the issue of inconsistent estimates from multi-source sensors in distributed cooperative passive localization and tracking, this paper proposes a distributed consistency algorithm based on Fast Covariance Interaction （FCI）。 First, a multi-sensor temporal registration model is constructed using the Taylor formula and adaptive density clustering theory to resolve temporal asynchrony caused by multi-source asynchronous sampling. Subsequently, each sensor performs local estimation by integrating information contributions from neighboring sensors. Next, a theoretical relationship between the iteration number and the consensus error is derived based on a given steady-state consensus error, ensuring that the information group converges to consensus within a finite number of iterations. On this basis, each sensor only needs to perform one global fast covariance interaction to complete information fusion, significantly improving the efficiency and accuracy of fusion. Finally, the algorithm is applied to a multi-source distributed collaborative passive positioning system. Simulation results demonstrate that the proposed method can effectively achieve consensus fusion positioning within a finite number of iterations while simultaneously completing temporal alignment.