Leveraging Compressive Sensing for Multiple Target Localization and Power Estimation in Wireless Sensor Networks

Peng QIAN; Yan GUO; Ning LI; Baoming SUN

doi:10.1587/transcom.2016EBP3333

Leveraging Compressive Sensing for Multiple Target Localization and Power Estimation in Wireless Sensor Networks

Peng QIAN, Yan GUO, Ning LI, Baoming SUN

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The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

Publication: IEICE TRANSACTIONS on Communications Vol.E100-B No.8 pp.1428-1435

Publication Date: 2017/08/01

Publicized: 2017/02/09

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2016EBP3333

Type of Manuscript: PAPER

Category: Network

Authors

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

Keyword

multiple target localization, transmitting power estimation, sensing matrix reconstruction, compressive sensing, wireless sensor networks

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Peng QIAN, Yan GUO, Ning LI, Baoming SUN, "Leveraging Compressive Sensing for Multiple Target Localization and Power Estimation in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications, vol. E100-B, no. 8, pp. 1428-1435, August 2017, doi: 10.1587/transcom.2016EBP3333.
Abstract: The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.2016EBP3333/_p

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@ARTICLE{e100-b_8_1428,
author={Peng QIAN, Yan GUO, Ning LI, Baoming SUN, },
journal={IEICE TRANSACTIONS on Communications},
title={Leveraging Compressive Sensing for Multiple Target Localization and Power Estimation in Wireless Sensor Networks},
year={2017},
volume={E100-B},
number={8},
pages={1428-1435},
abstract={The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.},
keywords={},
doi={10.1587/transcom.2016EBP3333},
ISSN={1745-1345},
month={August},}

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TY - JOUR
TI - Leveraging Compressive Sensing for Multiple Target Localization and Power Estimation in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1428
EP - 1435
AU - Peng QIAN
AU - Yan GUO
AU - Ning LI
AU - Baoming SUN
PY - 2017
DO - 10.1587/transcom.2016EBP3333
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E100-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2017
AB - The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.
ER -