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Title: Robust Kriged Kalman Filtering

Abstract

Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
NREL Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1257328
Report Number(s):
NREL/CP-5D00-65511
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2015 49th Asilomar Conference on Signals, Systems and Computers, 8-11 November 2015, Pacific Grove, California; Related Information: Proceedings of the 2015 49th Asilomar Conference on Signals, Systems and Computers, 8-11 November 2015, Pacific Grove, California
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; robust estimation; kriging; Kalman filter; sparsity; IP path delay monitoring; power consumption monitoring

Citation Formats

Baingana, Brian, Dall'Anese, Emiliano, Mateos, Gonzalo, and Giannakis, Georgios B. Robust Kriged Kalman Filtering. United States: N. p., 2015. Web. doi:10.1109/ACSSC.2015.7421400.
Baingana, Brian, Dall'Anese, Emiliano, Mateos, Gonzalo, & Giannakis, Georgios B. Robust Kriged Kalman Filtering. United States. doi:10.1109/ACSSC.2015.7421400.
Baingana, Brian, Dall'Anese, Emiliano, Mateos, Gonzalo, and Giannakis, Georgios B. Wed . "Robust Kriged Kalman Filtering". United States. doi:10.1109/ACSSC.2015.7421400.
@article{osti_1257328,
title = {Robust Kriged Kalman Filtering},
author = {Baingana, Brian and Dall'Anese, Emiliano and Mateos, Gonzalo and Giannakis, Georgios B.},
abstractNote = {Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.},
doi = {10.1109/ACSSC.2015.7421400},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Nov 11 00:00:00 EST 2015},
month = {Wed Nov 11 00:00:00 EST 2015}
}

Conference:
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