Multi-Rate Moving Horizon Estimation with Erroneous Infrequent Measurements Recovery

Scibilia, Francesco; Hovd, Morten

doi:10.3182/20090630-4-es-2003.00171

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where x is the estimated state, P ∈ IR n x ×n x is the estimated error covariance matrix of the state estimate, R ∈ IR n y ×n y the measurement noise covariance matrix and Q ∈ IR n x ×n x the process noise covariance matrix. The matrix Q and R can be seen, in addition to their statistical interpretation, as a measure of confidence in the model equations and the process data, and hence be regarded as tuning parameters (Scibilia and Hovd, 2009). The horizon contains (Γ− M +1) measurements, taken at times t k=M < ... < t k=Γ .…”

Section: The Moving Horizon Estimatormentioning

confidence: 99%

Comparison of statistical iceberg forecast models

Andersson

Scibilia

Copland

et al. 2018

Cold Regions Science and Technology

Self Cite

View full text Add to dashboard Cite

Short-term iceberg drift prediction is challenging. Large uncertainties in the driving forces -current, wind and waves -usually prevent accurate forecasts. Recently several statistical iceberg forecast models have been proposed by the authors, which use iceberg position measurements to improve the short-term drift forecast. In this article these statistical forecast methods and models are briefly reviewed. An extensive comparison between the statistical models, in addition to a dynamic iceberg forecast model, is performed on several iceberg drift trajectories. Based on this comparison a new statistical forecast scheme is proposed that combines some of the advantages of the other methods.

show abstract

Section: The Moving Horizon Estimatormentioning

confidence: 99%