Home // ICWMC 2013, The Ninth International Conference on Wireless and Mobile Communications // View article
Authors:
Thu Lam Ngoc Nguyen
Jaejin Lee
Yoan Shin
Keywords: wireless sensor network; multiple target localization; compressive sensing; deterministic sensing matrix; Reed-Muller codes.
Abstract:
Compressive sensing is a new signal processing technique for efficient reconstruction of an $n$-dimensional signal from $m~(mll n)$ measurements. Most of compressive sensing researches are based on randomization, while research on deterministic sampling is essential for practical implementation. In this paper, we study $mtimes n$ deterministic binary sensing matrices using second-order Reed-Muller codes, which satisfy a statistically restricted isometry property with reduced complexity for an application of multiple target localization in wireless sensor networks. We formulate multiple target locations as a sparse matrix, then exploit received signal strength information to recover noisy information using the deterministic sensing matrices and greedy algorithms to locate each target. The simulation results show that our scheme also achieves high accuracy in terms of localization errors when compared to traditional approaches with the random sensing matrices.
Pages: 185 to 189
Copyright: Copyright (c) IARIA, 2013
Publication date: July 21, 2013
Published in: conference
ISSN: 2308-4219
ISBN: 978-1-61208-284-4
Location: Nice, France
Dates: from July 21, 2013 to July 26, 2013