多目标融合与最小信息损失
Multiobject fusion with minimum information loss
AI总结：
本文研究了在多目标跟踪和映射中，通过最小化信息损失来融合多目标密度的方法，提出了一种与线性意见池（LOP）一致的融合规则，并解决了多目标融合中融合后的密度不属于原始家族的问题。
摘要：
广义协方差交（GCI）在多目标跟踪和映射中有效地融合了多个代理的多目标密度。从信息论的角度来看，已证明GCI融合本质上最小化了从局部密度到融合密度的加权信息增益（WIG）。本文关注一种同时最小化加权信息损失（WIL）的融合规则，发现这种融合规则与所谓的线性意见池（LOP）一致。然而，LOP无法直接应用于多目标融合，因为融合后的多目标密度（FMD）通常不再属于原始密度的同一族，因此不能作为贝叶斯多目标滤波中下一次递归的先验信息。为克服这一困难，进一步利用最小化WIL的原则，寻找属于原始族的最优FMD。讨论了所提出最小WIL（MWIL）融合规则的实现问题。最后，通过无线传感器网络上的分布式多目标跟踪仿真实验评估了MWIL规则的性能。
英文摘要：
Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the weighted information loss (WIL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), 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 the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing WIL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum WIL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
