Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/121448
Title: An Efficient Counting and Localization Framework for Off-Grid Targets in WSNs
Authors: Baoming Sun;Yan Guo;Ning Li;Dagang Fang
Year: 2017
Publisher: IEEE
Abstract: In this letter, we study the counting and localization problem for off-grid targets in wireless sensor networks. Existing compressed sensing-based schemes implicitly assume that all targets fall on a pre-defined grid exactly. However, when the assumption is violated, their performance deteriorates dramatically. To address this, we propose a novel counting and localization framework for off-grid targets. We first approximate the true and unknown sparsifying dictionary with its first order Taylor expansion around a known dictionary, and then formulate the counting and localization problem as a sparse recovery problem that recovers two sparse vectors with the same support. At last, we solve the problem using a variational Bayesian expectation-maximization algorithm. Simulation results highlight the superior performance of the proposed framework in terms of probability of correct counting and average localization error.
URI: http://localhost/handle/Hannan/121448
volume: 21
issue: 4
More Information: 809,
812
Appears in Collections:2017

Files in This Item:
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7797153.pdf471.91 kBAdobe PDF
Title: An Efficient Counting and Localization Framework for Off-Grid Targets in WSNs
Authors: Baoming Sun;Yan Guo;Ning Li;Dagang Fang
Year: 2017
Publisher: IEEE
Abstract: In this letter, we study the counting and localization problem for off-grid targets in wireless sensor networks. Existing compressed sensing-based schemes implicitly assume that all targets fall on a pre-defined grid exactly. However, when the assumption is violated, their performance deteriorates dramatically. To address this, we propose a novel counting and localization framework for off-grid targets. We first approximate the true and unknown sparsifying dictionary with its first order Taylor expansion around a known dictionary, and then formulate the counting and localization problem as a sparse recovery problem that recovers two sparse vectors with the same support. At last, we solve the problem using a variational Bayesian expectation-maximization algorithm. Simulation results highlight the superior performance of the proposed framework in terms of probability of correct counting and average localization error.
URI: http://localhost/handle/Hannan/121448
volume: 21
issue: 4
More Information: 809,
812
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7797153.pdf471.91 kBAdobe PDF
Title: An Efficient Counting and Localization Framework for Off-Grid Targets in WSNs
Authors: Baoming Sun;Yan Guo;Ning Li;Dagang Fang
Year: 2017
Publisher: IEEE
Abstract: In this letter, we study the counting and localization problem for off-grid targets in wireless sensor networks. Existing compressed sensing-based schemes implicitly assume that all targets fall on a pre-defined grid exactly. However, when the assumption is violated, their performance deteriorates dramatically. To address this, we propose a novel counting and localization framework for off-grid targets. We first approximate the true and unknown sparsifying dictionary with its first order Taylor expansion around a known dictionary, and then formulate the counting and localization problem as a sparse recovery problem that recovers two sparse vectors with the same support. At last, we solve the problem using a variational Bayesian expectation-maximization algorithm. Simulation results highlight the superior performance of the proposed framework in terms of probability of correct counting and average localization error.
URI: http://localhost/handle/Hannan/121448
volume: 21
issue: 4
More Information: 809,
812
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7797153.pdf471.91 kBAdobe PDF