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Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks

机译:基于扩散的EM算法在无线传感器网络中的高斯混合分布估计

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Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed EM algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach.
机译:高斯混合物的分布式估计在无线传感器网络(WSN)中具有许多应用,其节能解决方案仍然具有挑战性。针对这一问题,本文提出了一种新颖的基于扩散的EM算法。引入了一种扩散策略来获取EM算法中的全局统计信息,其中每个传感器节点仅需要在每次迭代时将其本地统计信息传递给其相邻节点。这改进了现有的基于共识的分布式EM算法,可能需要更多的通信开销来达成共识,尤其是在大规模网络中。所提出方法的鲁棒性和可扩展性可以通过网络中的分布式处理来实现。另外,我们表明,所提出的方法可以被认为是一种随机近似方法,可以找到高斯混合的最大似然估计。仿真结果表明了该方法的有效性。

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