# Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering

@article{Das2020OptimalSP,
title={Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering},
journal={arXiv: Signal Processing},
year={2020}
}
• Published 12 March 2020
• Computer Science, Mathematics
• arXiv: Signal Processing
5 Citations

## Figures from this paper

Sparse Sensing and Optimal Precision: Robust H∞ Optimal Observer Design with Model Uncertainty
• Mathematics, Computer Science
2021 American Control Conference (ACC)
• 2021
We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model uncertainty. The
Sensor Selection and Optimal Precision in $\mathcal{H}_2/\mathcal{H}_{\infty}$ Estimation Framework: Theory and Algorithms
• Computer Science
• 2021
The proposed integrated framework formulates the precision minimization as a convex optimization problem subject to linear matrix inequalities, and it is solved using an algorithm based on the alternating direction method of multipliers (ADMM).
Sparse Sensing and Optimal Precision: Robust $\mathcal{H}_{\infty}$ Optimal Observer Design with Model Uncertainty
• Computer Science, Mathematics
• 2020
A framework is presented which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model uncertainty, which is posed as a convex optimization problem subject to linear matrix inequalities.
Sparse Sensing and Optimal Precision: An Integrated Framework for H2/H∞ Optimal Observer Design
• Computer Science
IEEE Control Systems Letters
• 2021
The optimal sensor precision and the observer gain are determined, which achieves the specified accuracy in the state estimates, and the results presented in this letter are applied to the linearized longitudinal model of an F-16 aircraft.
Sparse Sensing for $\mathcal{H}_2/\mathcal{H}_{\infty}$ Optimal Observer Design with Bounded Errors
• Mathematics, Computer Science
• 2020
This paper simultaneously determines the optimal sensor precision and the observer gain, which achieves the specified accuracy in the state estimates, which is applied to the linearized longitudinal model of an F-16 aircraft.

## References

SHOWING 1-10 OF 44 REFERENCES
Sparsity-promoting adaptive sensor selection for non-linear filtering
• Computer Science
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 2014
This work considers the problem of adaptive sensor selection for applications in which the observations follow a non-linear model, e.g., target/bearing tracking, and designs a sparse selection vector based on the dynamical state model and state estimate.
Sensor placement for optimal Kalman filtering: Fundamental limits, submodularity, and algorithms
• Mathematics, Computer Science
2016 American Control Conference (ACC)
• 2016
It is proved that the minimum mean square error of the Kalman filter decreases only linearly as the number of sensors increases, a fundamental design limit, and an efficient approximation algorithm is provided that selects a small number sensors so to optimize theKalman filter with respect to this estimation error.
Optimal Sensor Selection via Proximal Optimization Algorithms
• Computer Science
2018 IEEE Conference on Decision and Control (CDC)
• 2018
This work proposes a customized proximal gradient method that scales better than standard SDP solvers and investigates alternative second-order extensions using the forward-backward quasi-Newton method for optimal sensor selection in large-scale dynamical systems.
Sparsity-Promoting Sensor Selection for Non-Linear Measurement Models
• Computer Science
IEEE Transactions on Signal Processing
• 2015
This paper focuses on observations that are related to a general non-linear model and forms the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex ℓ0-(quasi) norm optimization problem.
The unscented Kalman filter for nonlinear estimation
• Mathematics
Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373)
• 2000
This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A central and vital
Sensor Selection via Convex Optimization
• Computer Science
IEEE Transactions on Signal Processing
• 2009
This paper describes a heuristic, based on convex optimization, that gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements.
Optimal periodic scheduling of sensor networks: A branch and bound approach
• Mathematics, Computer Science
Syst. Control. Lett.
• 2013