论文标题
自适应混合物近似，用于杂物中的目标跟踪
Adaptive mixture approximation for target tracking in clutter
论文作者
论文摘要
目标跟踪代表了许多实际情况，例如空中交通管制，自动驾驶汽车，海洋雷达监视等。从贝叶斯的角度来看，当存在像杂物之类的现象时，绝大多数现有的跟踪算法必须处理可以随着时间的时间增长的关联假设。在这种情况下，后验分布可能会在计算上棘手，并且必须引入近似值。在这项工作中，根据使用的计算资源和跟踪性能，研究了假设数量和相应减少的影响。为此，考虑了最近开发的自适应混合物模型还原算法，以评估其在存在混乱的情况下将单个对象跟踪问题应用于单个对象跟踪的问题，并就解决问题的问题提供其他见解。
Target tracking represents a state estimation problem recurrent in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, the vast majority of the existing tracking algorithms have to deal with association hypotheses which can grow in the number over time; in that case, the posterior state distribution can become computationally intractable and approximations have to be introduced. In this work, the impact of the number of hypotheses and corresponding reductions is investigated both in terms of employed computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide additional insights on the addressed problem.
