Compressive Sensing Meets Dictionary Mismatch: Taylor Approximation-Based Adaptive Dictionary Algorithm for  Multiple Target Localization in WSNs

Yan GUO; Baoming SUN; Ning LI; Peng QIAN

doi:10.1587/transcom.2016EBP3366

Compressive Sensing Meets Dictionary Mismatch: Taylor Approximation-Based Adaptive Dictionary Algorithm for Multiple Target Localization in WSNs

Yan GUO, Baoming SUN, Ning LI, Peng QIAN

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Summary :

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

Publication: IEICE TRANSACTIONS on Communications Vol.E100-B No.8 pp.1397-1405

Publication Date: 2017/08/01

Publicized: 2017/01/24

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2016EBP3366

Type of Manuscript: PAPER

Category: Network

Authors

Yan GUO
  PLA University of Science and Technology
Baoming SUN
  PLA University of Science and Technology
Ning LI
  PLA University of Science and Technology
Peng QIAN
  PLA University of Science and Technology

Keyword

wireless sensor networks, compressive sensing, dictionary mismatch, adaptive dictionary, Taylor approximation, localization

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Yan GUO, Baoming SUN, Ning LI, Peng QIAN, "Compressive Sensing Meets Dictionary Mismatch: Taylor Approximation-Based Adaptive Dictionary Algorithm for Multiple Target Localization in WSNs" in IEICE TRANSACTIONS on Communications, vol. E100-B, no. 8, pp. 1397-1405, August 2017, doi: 10.1587/transcom.2016EBP3366.
Abstract: Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.2016EBP3366/_p

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@ARTICLE{e100-b_8_1397,
author={Yan GUO, Baoming SUN, Ning LI, Peng QIAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Compressive Sensing Meets Dictionary Mismatch: Taylor Approximation-Based Adaptive Dictionary Algorithm for Multiple Target Localization in WSNs},
year={2017},
volume={E100-B},
number={8},
pages={1397-1405},
abstract={Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.},
keywords={},
doi={10.1587/transcom.2016EBP3366},
ISSN={1745-1345},
month={August},}

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TY - JOUR
TI - Compressive Sensing Meets Dictionary Mismatch: Taylor Approximation-Based Adaptive Dictionary Algorithm for Multiple Target Localization in WSNs
T2 - IEICE TRANSACTIONS on Communications
SP - 1397
EP - 1405
AU - Yan GUO
AU - Baoming SUN
AU - Ning LI
AU - Peng QIAN
PY - 2017
DO - 10.1587/transcom.2016EBP3366
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E100-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2017
AB - Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.
ER -