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Title: Robust Kriged Kalman Filtering

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:
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
Publisher:
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
NREL Laboratory Directed Research and Development (LDRD)
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