NDLI: Trimmed diffusion least mean squares for distributed estimation

Content Provider	IEEE Xplore Digital Library
Author	Hong Ji Xiaohan Yang Badong Chen
Copyright Year	2015
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Xi'an Jiaotong Univ., Xi'an, China (Hong Ji; Xiaohan Yang; Badong Chen)
Abstract	We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate network parameters from noisy measurements. The problem is important when modeling a wide class of real-time sensor networks, where efficiency, robustness, and low power consumption are desired features. In this work, we focus on diffusion-based adaptive solutions that capable to avoid undue influence from outliers, especially in the presence of impulsive noise or dysfunction of certain nodes. We motivate and propose trimmed diffusion least mean square (TDLMS) algorithm that selects normal neighborhood to update the system estimation. We provide performance analysis together with simulation results comparing with existing methods.
Starting Page	643
Ending Page	646
File Size	126352
Page Count	4
File Format	PDF
ISSN	21653577
e-ISBN	9781479980581
DOI	10.1109/ICDSP.2015.7251953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-21
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Estimation Robustness Adaptive systems Signal processing algorithms Noise measurement diffusion least mean square Adaptive networks diffusion adaptation distributed estimation
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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