论文标题
多扫描多传感器多对象状态估计
Multi-Scan Multi-Sensor Multi-Object State Estimation
论文作者
论文摘要
如果计算障碍不是问题，则多对象估计应集成多个扫描中多个传感器的所有测量值。在本文中，我们通过计算（标记的）多传感器多对象后密度来提出一个有效的数值解决方案，以对多扫描多传感器多对象估计问题。最小化$ l_ {1} $ - 确切的后验密度中的标准错误需要解决NP-HARD的大规模多维分配问题。有效的多维分配算法是基于Gibbs采样以及收敛分析开发的。可以在一批或递归中脱机地应用所得的多扫描多传感器多对象估计算法。使用模拟数据集的数值实验证明了该算法的功效。
If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across multiple scans. In this article, we propose an efficient numerical solution to the multi-scan multi-sensor multi-object estimation problem by computing the (labeled) multi-sensor multi-object posterior density. Minimizing the $L_{1}$-norm error from the exact posterior density requires solving large-scale multi-dimensional assignment problems that are NP-hard. An efficient multi-dimensional assignment algorithm is developed based on Gibbs sampling, together with convergence analysis. The resulting multi-scan multi-sensor multi-object estimation algorithm can be applied either offline in one batch or recursively. The efficacy of the algorithm is demonstrated using numerical experiments with a simulated dataset.
