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Found 8 papers, 0 papers with code
Optimal State Estimation in the Presence of Non-Gaussian Uncertainty via Wasserstein Distance Minimization
The goal is to determine an optimal map combining prior estimate with measurement likelihood such that posterior estimation error optimally reaches the Dirac delta distribution with minimal effort.
A Convex Optimization Framework for Computing Robustness Margins of Kalman Filters
This paper proposes a novel convex optimization framework for designing robust Kalman filters that guarantee a user-specified steady-state error while maximizing process and sensor noise.
Quantifying Maximum Actuator Degradation for a Given $H_2/H_{\infty}$ Performance with Full-State Feedback Control
In this paper, we address the issue of quantifying maximum actuator degradation in linear time-invariant dynamical systems.
Privacy-aware Gaussian Process Regression
The key idea of the proposed method is to add synthetic noise to the data until the predictive variance of the Gaussian process model reaches a prespecified privacy level.
Optimal Sensor Precision for Multi-Rate Sensing for Bounded Estimation Error
We address the problem of determining optimal sensor precisions for estimating the states of linear time-varying discrete-time stochastic dynamical systems, with guaranteed bounds on the estimation errors.
Sensor Placement with Optimal Precision for Temperature Estimation of Battery Systems
Therefore, efficient, safe, and reliable battery system operation requires an accurate estimation of the temperature field.
Sensor Selection and Optimal Precision in $\mathcal{H}_2/\mathcal{H}_{\infty}$ Estimation Framework: Theory and Algorithms
We consider the problem of sensor selection for designing observer and filter for continuous linear time invariant systems such that the sensor precisions are minimized, and the estimation errors are bounded by the prescribed $\mathcal{H}_2/\mathcal{H}_{\infty}$ performance criteria.
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates
In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy.
