Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

—In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as fo...

• Type: Conference
• Year: 2009
• Where: GLOBECOM
• Authors: Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Jörg Widmer, Michele Zorzi
• Keywords: Data Gathering Point, GLOBECOM 2009, Real World Signals, Small Number, Telecommunications