Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Song, Il Young; Shin, Vladimir

doi:10.1049/iet-cta.2013.0179

Cited by 10 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, Figures 6(a) and 6(b) present the comparison of estimation results on location and velocity errors, respectively. The estimators of DFIR in Section 3 and DRHE [25] are chosen as the comparison with our proposed AFGE algorithm.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Compared to the previous DSE problems [5,20,25], a typical feature here is that the observations in x {N} and z {N} are mutually complementary; that is, each local observation of node can only be derived through the fusion of multiple…”

Section: Nns Model and Problem Formulationmentioning

confidence: 99%

“…A distributed fusion receding horizon estimator (DRHE) in [25] is adopted to compare with DFIR in Section 3 and the AFGE in Section 4. Let RMSE denote the root mean square error for the estimation of [ ] ; that is,…”

Section: Adaptive Finite-horizon Group Estimatormentioning

confidence: 99%

“…However, finite-horizon estimation approach has its own limitations, especially on expensively computational burden [22], with the reasons in terms of the adoption of finite recent measurements, the inversion computation [23], and the singularity problem [24]. One of the improvement methods is to manage the horizon size (window length), and several approaches [25] have been proposed. Although the existing approaches are effective with linear time-invariant systems, they can be less effective with wireless networked systems, with the reason that the constant (i.e., fixed) horizon size of the existing methods may be not able to handle the network-induced constraints owing to mixed LOS/NLOS environment.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Gao

Zhao

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

show abstract

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Nns Model and Problem Formulationmentioning

confidence: 99%

Section: Adaptive Finite-horizon Group Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Gao

Zhao

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…From (45) to (48) and (57), the robust local steady-state Kalman filters and smoothers are obtained aŝ…”

Section: Lemmamentioning

confidence: 99%

Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

RanChenjian

DENGZi-Li

2020

Adaptive Control & Signal

View full text Add to dashboard Cite

In this article, the robust distributed fusion Kalman filtering problems are addressed for the networked mixed uncertain multisensor systems with random one-step measurement delays, multiplicative noises, and uncertain noise variances. A new augmented state approach with fictitious measurement noises modeled by the first-order moving average models is presented, by which the original system is transformed into a standard uncertain system only with uncertain-variance fictitious white noises. Based on the minimax robust estimation principle and Kalman filtering theory, a universal integrated covariance intersection (ICI) fusion approach is presented in the sense that first of all the robust local estimators and their conservative error variances and crosscovariances are presented, and then integrating the local estimation information yields ICI fusers. An extended Lyapunov equation approach with two kinds of Lyapunov equations is presented in order to prove the robustness and to compute fictitious noise statistics. Applying these approaches, the minimax robust local, ICI, and fast ICI fused Kalman estimators (predictor, filter, and smoother) are presented, such that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. Their robustness, accuracy relations, and convergence are also proved. The proposed ICI fusers improve the robust accuracies and overcome the drawbacks of the original covariance intersection fusers, such that the robust local estimators and their conservative variances are assumed to be known, and their conservative crosscovariances are ignored. Two simulation examples applied to the offshore platform system verify their correctness, effectiveness, and applicability. K E Y W O R D S integrated covariance intersection fusion approach, minimax robust Kalman estimators, mixed-uncertain networked systems, multiplicative noises, random measurement delays, uncertain noise variances

show abstract

Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties

Ran

Deng

2021

Aerospace Science and Technology

View full text Add to dashboard Cite

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Cited by 10 publications

References 37 publications

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties

Contact Info

Product

Resources

About