论文标题
建模和减轻信念传播中的错误以进行分布式检测
Modeling and Mitigating Errors in Belief Propagation for Distributed Detection
论文作者
论文摘要
我们研究了由无线传感器网络（WSN）中错误数据交换影响的信念传播（BP）算法的行为。 WSN进行了分布式二进制假设检验，其中传感器观察的关节统计行为是由马尔可夫随机场建模的，该字段用于构建在传感节点之间交换的BP消息。通过BP消息更新规则的线性化，我们分析了由此产生的错误决策变量的行为，并得出了封闭形式的关系，这些关系描述了随机错误对BP算法性能的影响。然后，我们开发一个分散的分布式优化框架，通过通过分布式线性数据融合方案来减轻错误的影响来增强系统性能。最后，我们将所提出的分析的结果与现有作品进行了比较，并通过计算机模拟可视化通过提出的优化获得的性能增益。
We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed binary hypothesis test where the joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distributed optimization framework to enhance the system performance by mitigating the impact of errors via a distributed linear data-fusion scheme. Finally, we compare the results of the proposed analysis with the existing works and visualize, via computer simulations, the performance gain obtained by the proposed optimization.
