Sensor Integration by Joint PDF Construction using the Exponential Family

Kay Steven; Ding Quan; Rangaswamy Muralidhar

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Sensor Integration by Joint PDF Construction using the Exponential Family

机译：通过使用指数族的联合PDF构建进行传感器集成

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We investigate the problem of sensor integration to combine all the available information in a multi-sensor setting from a statistical standpoint. Specifically, we propose a novel method of constructing the joint probability density function (pdf) of the measurements from all the sensors based on the exponential family and small signal assumption. The constructed pdf only requires knowledge of the joint pdf under a reference hypothesis and, hence, is useful in many practical cases. Examples and simulation results show that our method requires less information compared with existing methods but attains comparable detection/classification performance.

机译：我们从统计的角度研究传感器集成问题，以在多传感器设置中组合所有可用信息。具体来说，我们提出了一种基于指数族和小信号假设构造所有传感器的测量的联合概率密度函数（pdf）的新颖方法。构造的pdf只需要在参考假设下了解联合pdf，因此在许多实际情况下很有用。实例和仿真结果表明，与现有方法相比，我们的方法需要的信息更少，但具有可比的检测/分类性能。

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来源
《Aerospace and Electronic Systems, IEEE Transactions on》 |2013年第1期|p.580-593|共14页
作者
Kay Steven; Ding Quan; Rangaswamy Muralidhar;
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作者单位

University of Rhode Island;

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原文格式 PDF
正文语种 eng
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关键词
Biomedical measurements; Joints; Maximum likelihood estimation; Probability density function; Radar; Training data; Vectors;

机译：生物医学测量;关节;最大似然估计;概率密度函数;雷达;培训数据;向量;

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Sensor Integration by Joint PDF Construction using the Exponential Family

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