IEICE TRANSACTIONS on Fundamentals
Several Bits Are Enough: Off-Grid Target Localization in WSNs Using Variational Bayesian EM Algorithm
Summary :
The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.7 pp.926-929
- Publication Date
- 2019/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.926
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Yan GUO
Army Engineering University
Peng QIAN
Army Engineering University
Ning LI
Army Engineering University
Keyword
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Yan GUO, Peng QIAN, Ning LI, "Several Bits Are Enough: Off-Grid Target Localization in WSNs Using Variational Bayesian EM Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 7, pp. 926-929, July 2019, doi: 10.1587/transfun.E102.A.926.
Abstract: The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.926/_p
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@ARTICLE{e102-a_7_926,
author={Yan GUO, Peng QIAN, Ning LI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Several Bits Are Enough: Off-Grid Target Localization in WSNs Using Variational Bayesian EM Algorithm},
year={2019},
volume={E102-A},
number={7},
pages={926-929},
abstract={The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.},
keywords={},
doi={10.1587/transfun.E102.A.926},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Several Bits Are Enough: Off-Grid Target Localization in WSNs Using Variational Bayesian EM Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 926
EP - 929
AU - Yan GUO
AU - Peng QIAN
AU - Ning LI
PY - 2019
DO - 10.1587/transfun.E102.A.926
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2019
AB - The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.
ER -
English
