Robust Kriged Kalman Filtering

Emiliano Dall-Anese, Brian Baingana, Gonzalo Mateo, Georgios Giannakis

Research output: Contribution to conferencePaper

5 Scopus Citations

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.
Original languageAmerican English
Pages1525-1529
Number of pages5
DOIs
StatePublished - 2015
Event2015 49th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, California
Duration: 8 Nov 201511 Nov 2015

Conference

Conference2015 49th Asilomar Conference on Signals, Systems and Computers
CityPacific Grove, California
Period8/11/1511/11/15

NREL Publication Number

  • NREL/CP-5D00-65511

Keywords

  • IP path delay monitoring
  • Kalman filter
  • kriging
  • power consumption monitoring
  • robust estimation
  • sparsity

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