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首页> 外文期刊>IEEE transactions on information forensics and security >Nonparametric Density Estimation, Hypotheses Testing, and Sensor Classification in Centralized Detection
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Nonparametric Density Estimation, Hypotheses Testing, and Sensor Classification in Centralized Detection

机译：集中检测中的非参数密度估计，假设检验和传感器分类

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摘要

In distributed sensing, the statistical model of the data collected by the sensor elements is often unavailable. In addition, these statistics may vary among the sensors and over time, for instance due to: 1) hardware variations; 2) the sensors' geographical locations; 3) different noise statistics; 4) diverse channel conditions between the sensor elements and the fusion center (FC); and 5) the presence of misbehaving sensors sending false data to the FC. In this paper, we consider the problem of centralized binary hypothesis testing in a wireless sensor network consisting of multiple classes of sensors, where the sensors are classified according to the probability density function (PDF) of their received data (at the FC) under each hypothesis. The sensor nodes transmit their observed data to the FC, which must classify the nodes and detect the state of nature. To optimally fuse the data, the FC must also estimate the PDFs of the sensors' observations. We develop a method based on the expectation maximization (EM) algorithm to estimate the PDFs for each sensor class, to classify the sensors, and to detect the underlying hypotheses. The estimation of PDFs is nonparametric in that no prior model is assumed. Simulation results using fewer than three iterations of the EM algorithm demonstrate the efficacy of the proposed method.
机译：在分布式感测中，通常无法获得由传感器元件收集的数据的统计模型。此外，这些统计信息可能会在传感器之间以及随时间变化，例如由于：1）硬件变化； 2）传感器的地理位置； 3）不同的噪声统计； 4）传感器元件与融合中心（FC）之间的不同通道条件； 5）存在行为不正常的传感器向FC发送错误数据。在本文中，我们考虑了由多个类别的传感器组成的无线传感器网络中的集中式二元假设检验问题，其中根据传感器在每个FC下的接收数据（在FC处）的概率密度函数（PDF）对其进行分类。假设。传感器节点将其观察到的数据传输到FC，FC必须对节点进行分类并检测自然状态。为了最佳地融合数据，FC还必须估计传感器观测值的PDF。我们开发了一种基于期望最大化（EM）算法的方法来估计每种传感器类别的PDF，对传感器进行分类并检测潜在的假设。 PDF的估计是非参数的，因为没有假定先验模型。使用少于3次的EM算法迭代进行的仿真结果证明了该方法的有效性。

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