Abstract / truncated to 115 words (read the full abstract)

Wireless sensor networks (WSNs) paved the way for accessing data previously unavailable by deploying sensors in various locations in space, each collecting local measurements of a target source signal. By exploiting the information resulting from the multitude of signals measured at the different sensors of the network, various tasks can be achieved, such as denoising or dimensionality reduction which can in turn be used, e.g., for source localization or detecting seizures from electroencephalography measurements. Spatial filtering consists of linearly combining the signals measured at each sensor of the network such that the resulting filtered signal is optimal in some sense. This technique is widely used in biomedical signal processing, wireless communication, and acoustics, among other ... toggle 5 keywords

distributed signal processing – spatial filtering – wireless sensor networks – adaptive filters – distributed optimization

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