A generalized ℓp-ℓq norm minimization approach for distributed estimation in sensor networks

A generalized ℓ p -ℓ q norm minimization approach for in-network distributed estimation is proposed. Different from the existing techniques which are assuming that all the nodes are affected by the same noise model, either Gaussian or non-Gaussian. We consider a general and practical scenario, the s...

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Main Authors: Fuxi Wen, Zhongmin Wang
Format: Conference Proceeding
Language:English
Subjects:
Cost function
distributed estimation
Estimation
Indexes
Kernel
Minimization
regularization
Robustness
sensor networks
Signal to noise ratio
uniformed framework
ℓ p -norm minimization
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Zhongmin Wang
description A generalized ℓ p -ℓ q norm minimization approach for in-network distributed estimation is proposed. Different from the existing techniques which are assuming that all the nodes are affected by the same noise model, either Gaussian or non-Gaussian. We consider a general and practical scenario, the spatially distributed nodes are affected by different noise models. To achieve robust estimation performance in different noise environments, each node solves a specific ℓ p -norm minimization problem corresponding to the noise model. Meanwhile, the ℓ q -norm penalty is imposed on the cost function to exploit prior information of the system, such as sparsity.
doi_str_mv 10.1109/TENCON.2016.7848246
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source IEEE Xplore All Conference Series
subjects Cost function
distributed estimation
Estimation
Indexes
Kernel
Minimization
regularization
Robustness
sensor networks
Signal to noise ratio
uniformed framework
ℓ p -norm minimization
title A generalized ℓp-ℓq norm minimization approach for distributed estimation in sensor networks
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