A Block-Based MC-SURE Algorithm for Denoising Sensor Data Streams
Mandoye Ndoye, USDOE, Chandrika Kamath · 2012
We propose a strategy to automatically denoise sensor data streams corrupted with noise that can be approximated as additive white Gaussian noise. The proposed block-based method is adapted from the Monte-Carlo-SURE (MC-SURE) algorithm which enables the blind optimization of the denoising parameter of a wide class of filters. Our framework is formulated by identifying and addressing the challenges that arise when the MC-SURE algorithm is applied in an on-line data processing setting, where latency (and the length of data blocks) must be constrained. The strategy has been tested using real datasets. Our results indicate that the proposed method can be effectively used to handle the denoising of real sensor streaming data that reasonably fit the Gaussian model assumption.