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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- NREL Laboratory Directed Research and Development (LDRD)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1257328
- Report Number(s):
- NREL/CP-5D00-65511
- 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
Similar Records
Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics
Kalman filtering to suppress spurious signals in Adaptive Optics control