2011Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Feasibility analysis of the robust adaptive Kalman filtering model

Huang Zhang-yu, Chen Xi-qiang

Open publisher page 0 citations

Abstract

Classic Kalman Filter is a dynamic and efficient data processing method, but there are some limitations. Robust estimation theory will be introduced to the Classical Kalman Filter (CKF) method, that is: Robust Adaptive Kalman Filter (RAKF). There is a clear advantage in reducing the observational errors and the state prediction errors context. In this paper, it uses a dam deformation monitoring example to illustrate that the RAKF is more reliable than the CKF in the deformation monitoring data processing effectively, and it is obviously in inhibiting the aspect of the state prediction errors and the observational errors. It is a viable and effective method of estimation method.

About this research paper

What this paper is about

Classic Kalman Filter is a dynamic and efficient data processing method, but there are some limitations. Robust estimation theory will be introduced to the Classical Kalman Filter (CKF) method, that is: Robust Adaptive Kalman Filter (RAKF). There is a clear advantage in reducing the observational errors and the state prediction errors context. In this paper, it uses a dam deformation monitoring example to illustrate that the RAKF is more reliable than the CKF in the deformation monitoring data processing effectively, and it is obviously in inhibiting the aspect of the state prediction errors and the observational errors. It is a viable and effective method of estimation method.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Classic Kalman Filter is a dynamic and efficient data processing method, but there are some limitations. Robust estimation theory will be introduced to the Classical Kalman Filter (CKF) method, that is: Robust Adaptive Kalman Filter (RAKF). There is a clear advantage in reducing the observational errors and the state prediction errors context. In this paper, it uses a dam deformation monitoring example to illustrate that the RAKF is more reliable than the CKF in the deformation monitoring data processing effectively, and it is obviously in inhibiting the aspect of the state prediction errors and the observational errors. It is a viable and effective method of estimation method.

Key concepts: Kalman filter, Computer science, Fast Kalman filter, Context (archaeology), Invariant extended Kalman filter, Adaptive filter, Control theory (sociology), Extended Kalman filter

Related papers

Back to paper searchBrowse research topicsOriginal source
Feasibility analysis of the robust adaptive Kalman filtering model — Research Paper | ScholarLens