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The Role of Bounded Fields-of-View and Negative Information in Finite Set Statistics (FISST)

机译:有界视野和负信息在有限集统计(FISST)中的作用

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The role of negative information is particularly important to search-detect-track problems in which the number of objects is unknown a priori, and the size of the sensor field-of-view is far smaller than that of the region of interest. This paper presents an approach for systematically 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 approach is derived for a representative set of multi-object distributions and demonstrated through a sensor planning problem involving a multi-Bernoulli process with up to one-hundred potential targets.
机译:负信息的作用对于先验未知物体数量且传感器视场大小远小于感兴趣区域的搜索-检测-跟踪问题尤其重要。本文提出了一种系统地将视场几何知识和位置以及对象包含/排除证据纳入对象状态密度和随机有限集多对象基数分布的方法。该方法是针对具有代表性的一组多对象分布而派生的,并通过涉及多达一百个潜在目标的多伯努利过程的传感器规划问题进行了演示。

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