Robust Kriged Kalman Filtering

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

Research output: Contribution to conferencePaper

5 Scopus Citations


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
Number of pages5
StatePublished - 2015
Event2015 49th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, California
Duration: 8 Nov 201511 Nov 2015


Conference2015 49th Asilomar Conference on Signals, Systems and Computers
CityPacific Grove, California

NREL Publication Number

  • NREL/CP-5D00-65511


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


Dive into the research topics of 'Robust Kriged Kalman Filtering'. Together they form a unique fingerprint.

Cite this