论文标题
轨迹集的向后模拟
Backward Simulation for Sets of Trajectories
论文作者
论文摘要
本文通过计算一系列Multitarget（未标记）过滤密度和Multitarget Dynamic模型，提出了用于恢复完整轨迹信息的解决方案。重要的是，提议的解决方案为未明确估算轨迹的多坐射过滤器开辟了轨迹估计的可能性。在本文中，我们首先根据轨迹集和随机有限设定框架得出一般的多标点前回向平滑方程。然后，我们展示了如何使用向后模拟进行轨迹集在多坐Multitarge滤波密度为多伯努利过程时。在仿真研究中证明了所提出的方法。
This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.
