在传感器网络中无需集中预白化，就能实现盲源分离。
Distributed Blind Source Separation based on FastICA
- 基于分布式信号融合框架，避开全局预白化步骤。
- 可分离出与通信开销线性相关的独立成分数量。
- 适合资源受限的无线传感器网络场景使用。
随着无线传感器网络（WSNs）的发展，许多传统信号处理任务需在分布式环境下进行，避免将原始数据传输至中心处理单元，以应对传感器有限的能耗和带宽。本文提出一种分布式独立成分分析（ICA）算法，旨在通过各传感器节点观测到的混合信号，还原原始信号源。经典的FastICA算法需进行空间预白化操作，但在带宽受限的分布式环境中，跨所有节点的全局预白化不可行，因其要求各通道间相互关联。本文证明，通过利用分布式自适应信号融合（DASF）框架的特性，可绕过显式的全网预白化步骤。尽管缺乏全局预白化，仍可获得中心化ICA解中的$Q$个最不高斯的独立成分，其中$Q$与所需通信负载呈线性关系。
原文摘要 · Abstract (English)
With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component analysis (ICA) algorithm, which aims at identifying the original signal sources based on observations of their mixtures measured at various sensor nodes. One of the most commonly used ICA algorithms is known as FastICA, which requires a spatial pre-whitening operation in the first step of the algorithm. Such a pre-whitening across all nodes of a WSN is impossible in a bandwidth-constrained distributed setting as it requires to correlate each channel with each other channel in the WSN. We show that an explicit network-wide pre-whitening step can be circumvented by leveraging the properties of the so-called Distributed Adaptive Signal Fusion (DASF) framework. Despite the lack of such a network-wide pre-whitening, we can still obtain the $Q$ least Gaussian independent components of the centralized ICA solution, where $Q$ scales linearly with the required communication load.
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