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Fusion for the detection of dependent signals using multivariate copulas

机译:融合以使用多变量copulas检测相关信号

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The use of multimodal or heterogeneous sensors for surveillance greatly increases the diversity of information available from a given region of interest. Since the underlying scene is the same for all the sensors, the data across the sensors are inherently dependent. The nature of this dependence can be quite complex and quantifying it is a challenging task, especially in the case of heterogeneous sensing. We consider the problem of fusion for the detection of dependent, heterogeneous signals and design a detector using a copula-based framework. Past applications using the copula based approach have mostly been limited to the bivariate (2 sensor) case. We will address copula construction and model selection issues for the multivariate case.
机译:使用多模式或异构传感器进行监视极大地增加了可从给定感兴趣区域获得的信息的多样性。由于所有传感器的基础场景都是相同的,因此传感器上的数据本质上是相关的。这种依赖性的性质可能非常复杂,对其进行量化是一项艰巨的任务,尤其是在异构传感的情况下。我们考虑用于检测依赖的异构信号的融合问题,并使用基于copula的框架设计检测器。过去使用基于copula的方法的应用大部分限于双变量(2传感器)情况。我们将针对多变量案例解决copula构造和模型选择问题。

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