Share Email Print

Proceedings Paper

Multisensor information compression and reconstruction
Author(s): Bing Du; Liang Liu; Jun Zhang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger than the sparsity of the sampled sensor data is needed for successful recovery.

Paper Details

Date Published: 13 April 2009
PDF: 11 pages
Proc. SPIE 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009, 73450P (13 April 2009); doi: 10.1117/12.817902
Show Author Affiliations
Bing Du, BeiHang Univ. (China)
Liang Liu, Beihang Univ. (China)
Jun Zhang, BeiHang Univ. (China)

Published in SPIE Proceedings Vol. 7345:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top