论文标题
分裂发生了！高斯混合物中的不精确和负面信息随机有限套件过滤
Split Happens! Imprecise and Negative Information in Gaussian Mixture Random Finite Set Filtering
论文作者
论文摘要
在对象跟踪和状态估计问题中，诸如不精确测量和缺乏检测之类的模棱两可的证据可以包含有价值的信息，因此可以利用以进一步完善概率信念状态。特别是，可以利用有关传感器有限视野的知识，以结合观察到的对象的证据。本文提出了一种系统的方法，用于结合视野几何形状，位置以及对象包含/排除证据中的知识，并将其纳入对象状态密度和随机有限的多对象基础性分布中。最终的状态估计问题是非线性的，并使用基于递归成分拆分的新高斯混合物近似解决。基于此近似，在跟踪问题中仅使用自然语言语句作为输入来得出并证明一种新型的高斯混合物Bernoulli滤波器，以进行不精确的测量。本文还考虑了代表性选择的多对象分布的界面视野和基数分布之间的关系，该分布可用于传感器计划，这是通过涉及多达一百个潜在对象的多重bernoulli过程的问题所证明的。
In object tracking and state estimation problems, ambiguous evidence such as imprecise measurements and the absence of detections can contain valuable information and thus be leveraged to further refine the probabilistic belief state. In particular, knowledge of a sensor's bounded field-of-view can be exploited to incorporate evidence of where an object was not observed. This paper presents a systematic approach for incorporating knowledge of the field-of-view geometry and position and object inclusion/exclusion evidence into object state densities and random finite set multi-object cardinality distributions. The resulting state estimation problem is nonlinear and solved using a new Gaussian mixture approximation based on recursive component splitting. Based on this approximation, a novel Gaussian mixture Bernoulli filter for imprecise measurements is derived and demonstrated in a tracking problem using only natural language statements as inputs. This paper also considers the relationship between bounded fields-of-view and cardinality distributions for a representative selection of multi-object distributions, which can be used for sensor planning, as is demonstrated through a problem involving a multi-Bernoulli process with up to one-hundred potential objects.
