The cross-covariances among local sensor estimates are usually unknown or can’t be accurately known in multi-sensor systems. The Covariance Intersection (CI), Convex Combination (CC), Largest Ellipsoid (LE) and Ellipsoidal Intersection (EI) algorithms have been developed for the estimate fusion with unknown cross-covariances. In this contribution, we reveal a strong commonality in principle among CI, CC, LE and EI algorithms after a transformation into a new Euclidean space, although each algorithm is designed based on different criteria. We also assess the consistencies of CC, LE and EI algorithms under different conditions which have been found significantly dependent on the correlation level among local estimates. All the CI, CC, LE and EI algorithms have the capability to enhance consistency or accuracy but at a cost of accuracy or consistency. Based on the commonality and consistency features among different algorithms, an improved algorithm is presented which can significantly improve the consistency performance at a very little expense of accuracy. The theoretical analysis and the fusion algorithm selection strategy are testified through simulated examples and the fusion of GPS and GLONASS horizontal position solutions.

在多传感器系统中，本地传感器估计值之间的互协方差通常是未知的或无法精确知道的。已经开发出协方差交叉点（CI），凸组合（CC），最大椭圆体（LE）和椭圆体交叉点（EI）算法，用于具有未知交叉协方差的估计融合。在此贡献中，尽管每种算法都是基于不同的标准设计的，但我们揭示了在转换为新的欧几里得空间后，CI，CC，LE和EI算法在原理上具有很强的共性。我们还评估了CC，LE和EI算法在不同条件下的一致性，这些条件在很大程度上取决于局部估计之间的相关性水平。所有的CI，CC，LE和EI算法均具有增强一致性或准确性的能力，但会降低准确性或一致性。基于不同算法之间的共性和一致性特征，提出了一种改进算法，可以以很少的精度代价就可以显着提高一致性性能。通过仿真算例以及GPS与GLONASS水平位置解的融合，论证了理论分析和融合算法的选择策略。