Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering
From MaRDI portal
Publication:2793963
DOI10.1002/acs.2552zbMath1333.93241arXiv1303.4622OpenAlexW3122770895WikidataQ57604755 ScholiaQ57604755MaRDI QIDQ2793963
No author found.
Publication date: 17 March 2016
Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1303.4622
adaptive filteringmaximum likelihood estimationKalman filteringfilter sensitivity computationlinear discrete-time stochastic systemssquare-root implementation
Related Items
Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory ⋮ On the computation of derivatives within LD factorization of parametrized matrices ⋮ A unified square-root approach for the score and Fisher information matrix computation in linear dynamic systems ⋮ Data filtering based maximum likelihood gradient estimation algorithms for a multivariate equation-error system with ARMA noise ⋮ Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering
Cites Work
- On efficient parametric identification methods for linear discrete stochastic systems
- Maximum likelihood estimation via the extended covariance and combined square-root filters
- Factorization methods for discrete sequential estimation
- On consistency of subspace methods for system identification
- Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements
- Extension of square-root filtering to include process noise
- Score Evaluation Within the Extended Square-Root Information Filter
- A new method for evaluating the log-likelihood gradient (score) of linear dynamic systems
- Stable adaptive observers for nonlinear time-varying systems
- A new method for evaluating the log-likelihood gradient, the Hessian, and the Fisher information matrix for linear dynamic systems
- Global adaptive observers for nonlinear systems via filtered transformations
- Optimal input signals for parameter estimation in dynamic systems--Survey and new results
- Stable adaptive schemes for state estimation and identification of linear systems
- Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations
- Convergence analysis of parametric identification methods
- Extended Chandrasekhar recursions
- Some new algorithms for recursive estimation in constant, linear, discrete-time systems
- New square-root algorithms for Kalman filtering
- Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
- Likelihood Gradient Evaluation Using Square-Root Covariance Filters
- Maximum likelihood estimation using square root information filters
- Evaluation of likelihood functions for Gaussian signals
- State Sensitivity Evaluation Within UD Based Array Covariance Filters
- Approaches to adaptive filtering
- Subspace-based identification: Weighting and pre-filtering of instruments
