论文标题
数据关联的不确定性建模和计算方面
Uncertainty modelling and computational aspects of data association
论文作者
论文摘要
提出并评估了多对象动力学系统平滑问题的新颖解决方案。感兴趣的系统包含一个未知数和不同数量的动态对象，这些对象在嘈杂和损坏的观察结果下部分观察到。为了说明缺乏有关此类复杂系统不同方面的信息，考虑了不确定性的替代表示。相应的统计模型可以作为由有条件独立的隐藏马尔可夫模型组成的分层模型。利用这种特殊的结构，以在马尔可夫链蒙特卡洛（MCMC）的背景下提出一种有效的方法，它以与粒子MCMC相似的方式依靠相应的过滤问题的近似解决方案。在一系列方案中，这种方法显示出胜过现有算法的表现。
A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical objects that are partially observed under noisy and corrupted observations. An alternative representation of uncertainty is considered in order to account for the lack of information about the different aspects of this type of complex system. The corresponding statistical model can be formulated as a hierarchical model consisting of conditionally-independent hidden Markov models. This particular structure is leveraged to propose an efficient method in the context of Markov chain Monte Carlo (MCMC) by relying on an approximate solution to the corresponding filtering problem, in a similar fashion to particle MCMC. This approach is shown to outperform existing algorithms in a range of scenarios.
