A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.

翻译：贝叶斯分散数据融合中的一个关键挑战是“谣言传播”或“重复计数”现象，即先前发送的数据循环返回至其发送者。该问题通常通过协方差交集（CI）等近似方法解决，这类方法通过对估计值进行加权平均来计算界。问题在于该界并不紧，即估计往往过于保守。本文表明，通过利用多智能体分散融合问题中的概率独立结构，可以找到更紧的界，方法包括：(i) 对CI算法进行扩展，使用多个（非整体型）加权因子替代原始CI中的单一（整体型）因子；(ii) 一种通用优化方案，能够计算最优界并充分利用任意依赖结构。我们对方法进行了比较，并展示在一个简单问题上它们收敛于同一解。随后，我们在大规模目标跟踪仿真中测试了新的非整体型CI算法，结果表明与原始整体型CI相比，该算法能实现更紧的界和更精确的估计。