139 citations · 649 across the 28 of their papers we have counts for
9 papers · 1 filter
Minimax-Optimal Robust Identification of Continuous-Time Systems: Handling Narrow-Band Disturbances in the Frequency Domain
Jonas Gillberg, Fredrik Gustafsson
The high-frequency spectral roll-off of continuous-time ARMA (CARMA) models can magnify the effect of narrow-band disturbances when aliasing is weak, making standard maximum-likeli…
On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model
Magnus Malmström, Isaac Skog, Daniel Axehill +1
The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the p…
Decentralized State Estimation In A Dimension-Reduced Linear Regression
Robin Forsling, Fredrik Gustafsson, Zoran Sjanic +1
Decentralized state estimation in a communication-constrained sensor network is considered. The exchanged estimates are dimension-reduced to reduce the communication load using a l…
State Estimation for Piecewise Affine State-Space Models
Rafael Rui, Tohid Ardeshiri, Henri Nurminen +2
We propose a filter for piecewise affine state-space (PWASS) models. In each filtering recursion, the true filtering posterior distribution is a mixture of truncated normal distrib…
Skew-t Filter and Smoother with Improved Covariance Matrix Approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piché +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based…
Skew-t inference with improved covariance matrix approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piche +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our ear…
Robust Inference for State-Space Models with Skewed Measurement Noise
Henri Nurminen, Tohid Ardeshiri, Robert Piché +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes…
Data-Driven Robust Receding Horizon Fault Estimation
Yiming Wan, Tamas Keviczky, Michel Verhaegen +1
This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustnes…
Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
Tohid Ardeshiri, Emre Özkan, Umut Orguner +1
We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique t…