The linear opinion pool (LinOP) provides a potential solution to the problem of information fusion. However, the LinOP cannot be directly applied to multi-object fusion since the resulting fused multi-object density, in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in Bayesian multi-object filtering. In this letter, by showing that the LinOP is actually the one that leads to minimum information loss (MIL), we propose to find the fused multi-object density that has the same form as the local ones and, at the same time, leads to MIL. The performance of MIL fusion is then compared with the one of the well-known generalized covariance intersection (GCI) fusion via simulations.

中文翻译：

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信息损失最小的多对象融合


线性意见池（LinOP）为信息融合问题提供了潜在的解决方案。然而，LinOP 不能直接应用于多对象融合，因为所得的融合多对象密度通常不再属于局部密度的同一族，因此不能用作下一次递归的先验信息。贝叶斯多对象过滤。在这封信中，通过证明 LinOP 实际上是导致最小信息损失（MIL）的方法，我们建议找到与局部密度具有相同形式的融合多对象密度，同时导致至军用。然后通过模拟将 MIL 融合的性能与众所周知的广义协方差交叉 (GCI) 融合之一进行比较。