Fast Diffusion Minimum Generalized Rank Norm Based on QR Decomposition

The outliers or impulsive noise is prominent in practical wireless sensor networks due to saturation effects, non-linearities, malfunction of sensors, environmental abnormalities, etc. The classical diffusion algorithms based on mean square error cost function are sensitive to outliers and their per...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2022-03, Vol.69 (3), p.1942-1946
Main Authors: Modalavalasa, Sowjanya, Sahoo, Suraj Prakash, Sahoo, Upendra Kumar, Sahoo, Ajit Kumar
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Convergence
Cost function
Decomposition
Diffusion rate
Diffusion strategy
Estimation
generalized rank norm
impulsive noise
Matrix decomposition
Outliers (statistics)
Parameter estimation
Performance degradation
QR decomposition
Robustness
Sensors
Signal processing algorithms
Wireless sensor networks
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Summary:The outliers or impulsive noise is prominent in practical wireless sensor networks due to saturation effects, non-linearities, malfunction of sensors, environmental abnormalities, etc. The classical diffusion algorithms based on mean square error cost function are sensitive to outliers and their performance degrades in the presence of outliers in either desired data or the input data. A fast diffusion minimum generalized rank norm based on QR decomposition (FDGR-QR) is proposed, which is robust against outliers in both desired and input data and has faster convergence than the state of the art algorithms. Different outlier percentages distribution across the network is considered for the simulations based experiments. The proposed algorithm is validated for both stationary and non-stationary parameter estimation scenarios.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2021.3125577