Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems

Rashedi, Mohammad; Liu, Jinfeng; Huang, Biao

doi:10.1016/j.ifacol.2015.08.174

Cited by 9 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We do not detail such a strategy since it is out of the scope of the present paper. Interested readers can refer to Rashedi, Liu, & Huang (2015) as a starting point in the framework of continuous systems. In the continuousdiscrete setting, Theorem 2.1 is required to prove the convergence.…”

Section:  Adaptive High-gain Extended Kalman Filter Casementioning

confidence: 99%

On the stability of a differential Riccati equation for continuous-discrete observers

Boizot

Busvelle²

2015

International Journal of Control

View full text Add to dashboard Cite

This article deals with the stability properties of a continuous-discrete Riccati differential equation. The main motivation comes from the theory of Kalman filters for continuous-time nonlinear systems with sampled measurements, where this type of equation often arises. Stability is to be understood in the following sense: under appropriate hypotheses, the solution matrix is always symmetric positive definite and bounded from above and below. No uniformity is expected from the sampling procedure, only an upper bound on the elapsed time between consecutive samples is needed. The exposition consists of two parts. First, the stability properties are proven and second, by means of three examples, it is shown how the article's main result applies.

show abstract

Section:  Adaptive High-gain Extended Kalman Filter Casementioning

confidence: 99%

On the stability of a differential Riccati equation for continuous-discrete observers

Boizot

Busvelle²

2015

International Journal of Control

View full text Add to dashboard Cite

show abstract

“…However, all existing recursive filtering algorithms to deal with the colored measurement noise are limited to the following assumption, that is, they are only applicable to the single sensor or multisensor centralized fusion structure, except for the covariance intersection‐based event‐triggered multi‐rate fusion estimation based on recursively calculating the upper bound of the filtering error covariance 32 which is neither Bayesian filtering nor optimal from the multisensor fusion perspective. In fact, considering the large‐scale sensing systems or sensor networks, the distributed filtering is always necessary, 33‐36 which is an important and hot topic, especially when the model nonlinearity meets the distributed fusion framework 37‐41 . Nevertheless, to the best of authors' knowledge, there is no research on the distributed recursive Bayesian filtering design for nonlinear systems with colored measurement noise in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time distributed block‐decomposed information filter for nonlinear systems with colored measurement noise

Yang

Qin

Yong

et al. 2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper considers the distributed filtering problem for discrete‐time nonlinear systems with colored measurement noise obeying a nonlinear autoregressive process in sensor networks. A novel block‐decomposed information‐type filter for such systems is proposed in a centralized fusion structure, by using the statistical linear regression to deal with model nonlinearities and the measurement difference approach to overcome the noise correlation caused by colored measurement noises. Meanwhile, with the help of block matrix inverse operation to realize the high‐dimensional block matrix decomposition, the information vector and information matrix of the original system state in the designed information filter is directly estimated recursively, so as to own good numerical stability. Then, the finite‐time distributed implementation of the proposed filter is put forward, where the final filtering estimate in each sensor node is consistent with the centralized filtering result, by ensuring that each sensor node directly obtains the average values of shared variables in the sensor network through finite iterations of average consensus. Finally, the posterior Cramér–Rao lower bound is derived to show the proposed filter reaches the optimal theoretical performance bound in the premise of statistical linear regression and Gaussian posterior probability density approximation. A target tracking example with colored measurement noise obeying a linear and a nonlinear autoregressive processes validates the proposed method.

show abstract

“…Due to the pervasive existence of nonlinearity in industrial systems, the nonlinear filtering problem has received tremendous research interest in the past few decades. Recently, a number of filtering algorithms have been proposed for nonlinear systems including extended Kalman filtering (EKF), 1‐3 strong tracking filtering (STF), 4 unscented Kalman filtering (UKF), 5,6 and particle filtering 7 . For nonlinear systems with Gaussian noises, the EKF was developed by linearizing the nonlinear function at estimation of states 1‐3 .…”

Section: Introductionmentioning

confidence: 99%

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Sheng

Niu

Gao

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown-but-bounded disturbances, and state-and disturbance-dependent noises ((x, d)-dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input-to-state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum-of-squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.

show abstract

Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems

Cited by 9 publications

References 16 publications

On the stability of a differential Riccati equation for continuous-discrete observers

On the stability of a differential Riccati equation for continuous-discrete observers

Finite‐time distributed block‐decomposed information filter for nonlinear systems with colored measurement noise

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

Contact Info

Product

Resources

About