Stochastic Geometry for Multi-sensor Data Fusion System

该项目的目的是在多传感器多目标环境中开发有效的跟踪和传感器管理算法。有限随机集理论提供了一种自然的方式来表示随机数（随机）对象状态，这个问题直到最近才在跟踪文献中被很大程度上忽略。尽管随机集理论已经为多目标滤波提供了令人满意的基础，但在这个早期阶段，还没有从这个框架中出现能够跟踪多目标的算法。我们相信，利用随机几何的洞察力和数学工具，可以开发出有效的算法

The aim of this project is to develop efficient algorithms for tracking and sensor management in a multi-sensor multi-target environment. Finite random set theory provides a natural way of representing a random number of (random) object states, an issue that has been largely ignored in the tracking literature until recently. Although a satisfactory foundation for multiple object filtering has been provided by random set theory, in this early stage no algorithm capable of tracking many targets has emerged from this framework. We are confident that efficient algorithms can be developed by exploiting the insights and mathematical tools of stochastic geometry