Least-mean-square receding horizon estimation
type: Item
instance of: scholarly article
title: Least-mean-square receding horizon estimation
zbMATH Open document ID: 1264.93237
DOI: 10.1155/2012/631759
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation. The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon. Any a priori state information is not required, and existing artificial constraints for easy derivation are not imposed. For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form. For numerical reliability, the iterative form is presented with forward and backward computations. It is shown through a numerical example that the proposed LMS RH estimator has better robust performance than conventional Kalman estimators when uncertainties exist.
Mathematics Subject Classification ID: 93E10
zbMATH DE Number: 6173440
author: Bokyu Kwon
author: Soohee Han
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/631759
OpenAlex ID: W1985080469
Wikidata QID: Q58912344
cites work: The stability of constrained receding horizon control
cites work: Stabilizing receding-horizon control of nonlinear time-varying systems
cites work: $L_{2}-E$ FIR Smoothers for Deterministic Discrete-Time State–Space Signal Models
cites work: A receding horizon Kalman FIR filter for discrete time-invariant systems
cites work: Q3993068
cites work: Fixed memory least squares filtering (Corresp.)
cites work: Fixed-memory recursive filters (Corresp.)
cites work: On optimal stochastic midcourse guidance
cites work: Receding horizon recursive state estimation
cites work: Minimum-Distance Receding-Horizon State Estimation for Switching Discrete-Time Linear Systems
cites work: Robust receding-horizon state estimation for uncertain discrete-time linear systems
cites work: A modified quadratic cost problem and feedback stabilization of a linear system
cites work: A receding horizon unbiased FIR for discrete-time state space models
cites work: Minimax FIR smoothers for deterministic continuous-time state space models
cites work: Robust \(H_\infty \) finite-horizon filtering with randomly occurred nonlinearities and quantization effects
cites work: On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements
description: scientific article; zbMATH DE number 6173440