提出一种基于广义协方差交集的分布式多目标滤波方法，提升融合精度。
Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection
- 用近似泊松多伯努利密度推导广义协方差交集融合规则
- 融合结果为可闭式表达的泊松多伯努利混合分布
- 适合分布式多目标跟踪场景，尤其适用于传感器网络
本文提出一种基于广义协方差交集（GCI）融合规则的分布式泊松多伯努利（PMB）滤波器，用于分布式多目标跟踪。由于两个PMB密度的精确GCI融合不可行，我们提出一种合理近似：将PMB密度的幂次近似为非归一化PMB密度，对应于PMB密度的上界。此时，GCI融合规则转化为两个非归一化PMB密度的归一化乘积。我们证明该结果为泊松多伯努利混合（PMBM），且可闭式表达。各滤波器中的未来预测与更新步骤均保持PMBM形式，可在下一次融合前投影回PMB密度。实验表明，该方法优于其他分布式多目标滤波器。
原文摘要 · Abstract (English)
This paper presents the distributed Poisson multi-Bernoulli (PMB) filter based on the generalised covariance intersection (GCI) fusion rule for distributed multi-object filtering. Since the exact GCI fusion of two PMB densities is intractable, we derive a principled approximation. Specifically, we approximate the power of a PMB density as an unnormalised PMB density, which corresponds to an upper bound of the PMB density. Then, the GCI fusion rule corresponds to the normalised product of two unnormalised PMB densities. We show that the result is a Poisson multi-Bernoulli mixture (PMBM), which can be expressed in closed form. Future prediction and update steps in each filter preserve the PMBM form, which can be projected back to a PMB density before the next fusion step. Experimental results show the benefits of this approach compared to other distributed multi-object filters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。