Recursive Bayesian estimation using piece-wise constant approximations
Automatica (Journal of IFAC)
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Brief paper: Bayesian estimation via sequential Monte Carlo sampling-Constrained dynamic systems
Automatica (Journal of IFAC)
Brief paper: Derivative-free estimation methods: New results and performance analysis
Automatica (Journal of IFAC)
On Kalman Filtering With Nonlinear Equality Constraints
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Brief paper: A detection-estimation scheme for state estimation in switching environments
Automatica (Journal of IFAC)
Technical Communique: Nonlinear bounded-error state estimation of continuous-time systems
Automatica (Journal of IFAC)
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Advanced point-mass method for nonlinear state estimation
Automatica (Journal of IFAC)
Digital synthesis of non-linear filters
Automatica (Journal of IFAC)
Recursive bayesian estimation using gaussian sums
Automatica (Journal of IFAC)
Saturated Particle Filter: Almost sure convergence and improved resampling
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
The paper focuses on the state estimation problem of nonlinear non-Gaussian systems with state subject to a nonlinear inequality constraint. Taking into account the available additional information about the state given by the constraint increases the estimate quality compared to classical state estimation methods which cannot utilize the information. Considering the constraint in the form of an inequality involving a nonlinear function of the state makes the state estimation problem difficult and hence treated only marginally. In this paper, a generic local filter for the inequality constrained estimation problem is proposed. It covers the extended Kalman filter, unscented Kalman filter, and divided difference filter as special cases and enforces the constraint by truncating the conditional density of the state. The truncation is computationally cheap, yet it provides high estimate quality of the constrained estimate. The same idea is then utilized in a truncation Gaussian mixture filter which is also proposed in the paper to increase the estimate quality further by providing a global constrained estimate. Superior estimate quality and computational efficiency of the proposed filters are illustrated in two numerical examples.