Stability of consensus extended Kalman filter for distributed state estimation

By Giorgio Battistelli, Luigi Chisci. at - Automatisierungstechnik, 2016

314 citations recorded by the source corpus.

The paper addresses consensus-based networked estimation of the state of a nonlinear dynamical system. The focus is on a family of distributed state estimation algorithms which relies on the extended Kalman filter linearization paradigm. Consensus is exploited in order to fuse the information, both prior and novel, available in each network node. It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability. A simulation case-study concerning target tracking with a network of nonlinear (angle and range) position sensors is worked out in order to show the effectiveness of the considered nonlinear consensus filter.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/5ff2f5ffd74269830a7574741a25ede58ec9dd30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qing-Long Han
2. Derui Ding
3. Zidong Wang
4. Xiaohua Ge
5. Xianming Zhang
6. Lei Ding

In Computer Networks and Communications

1. Qing-Long Han
2. Derui Ding
3. Zidong Wang
4. Xianming Zhang
5. Xiaohua Ge
6. Huijun Gao
7. Lei Ding
8. James Lam

Citations in the corpus

• A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks (2020, methodology): “…over sensor networks can be generally divided into three categories, namely, minimum-variance-based algorithms [e.g., Kalman filter and extended Kalman filters (EKFs)] [5], [23], H ∞ -performance-based algorithms [13], [24], [36], [43], and ellipsoid-set-based algorithms [6], [18], [31], [32], [42].”