Title :
Distributed Compressed Estimation Based on Compressive Sensing
Author :
Songcen Xu ; de Lamare, Rodrigo C. ; Poor, H. Vincent
Author_Institution :
Dept. of Electron., Univ. of York, York, UK
Abstract :
This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which can further improve the performance of the proposed distributed compressed estimation scheme. Simulations for a wireless sensor network illustrate the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.
Keywords :
compressed sensing; convergence of numerical methods; data compression; mean square error methods; sparse matrices; wireless sensor networks; compression module; compressive sensing; convergence rate; decompression module; distributed compressed estimation; mean square error method; measurement matrix optimization; sparse signal; wireless sensor network; Algorithm design and analysis; Compressed sensing; Estimation; Optimization; Signal processing algorithms; Vectors; Wireless sensor networks; Compressive sensing; distributed compressed estimation; measurement matrix optimization; sensor networks;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2400372
