2012•Journal of Aerospace PowerRequires access

Adaptive extended incremental Kalman filter method

Lou Tai-shan

Open publisher page 4 citations

Abstract

Based on extended incremental Kalman filter(EIKF) and adaptive incremental Kalman filter(AIKF),the adaptive extended incremental Kalman filter(AEIKF) and analysis method were put forward,the key calculative steps were established.The measurement equations have many uncertainties and can't obtain precision parameters in actual environment(such as deep space exploration).Due to environmental factors and the instability of measurement equipments,the measurement data have unknown time-varying system errors in actual engineering,which leads to greater Kalman filtering error.Ultimately,the convergence of Kalman filter were reduced.The presented adaptive extended incremental Kalman filter method can successfully eliminate these unknown system errors.The method can estimate statistical characteristics of noise in real time and greatly improve the accuracy of filter.The method is simple to calculate and easy to apply in engineering.

About this research paper

What this paper is about

Based on extended incremental Kalman filter(EIKF) and adaptive incremental Kalman filter(AIKF),the adaptive extended incremental Kalman filter(AEIKF) and analysis method were put forward,the key calculative steps were established.The measurement equations have many uncertainties and can't obtain precision parameters in actual environment(such as deep space exploration).Due to environmental factors and the instability of measurement equipments,the measurement data have unknown time-varying system errors in actual engineering,which leads to greater Kalman filtering error.Ultimately,the convergence of Kalman filter were reduced.The presented adaptive extended incremental Kalman filter method can successfully eliminate these unknown system errors.The method can estimate statistical characteristics of noise in real time and greatly improve the accuracy of filter.The method is simple to calculate and easy to apply in engineering.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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

Based on extended incremental Kalman filter(EIKF) and adaptive incremental Kalman filter(AIKF),the adaptive extended incremental Kalman filter(AEIKF) and analysis method were put forward,the key calculative steps were established.The measurement equations have many uncertainties and can't obtain precision parameters in actual environment(such as deep space exploration).Due to environmental factors and the instability of measurement equipments,the measurement data have unknown time-varying system errors in actual engineering,which leads to greater Kalman filtering error.Ultimately,the convergence of Kalman filter were reduced.The presented adaptive extended incremental Kalman filter method can successfully eliminate these unknown system errors.The method can estimate statistical characteristics of noise in real time and greatly improve the accuracy of filter.The method is simple to calculate and easy to apply in engineering.

Key concepts: Kalman filter, Fast Kalman filter, Alpha beta filter, Invariant extended Kalman filter, Control theory (sociology), Ensemble Kalman filter, Extended Kalman filter, Adaptive filter

Related papers

Back to paper searchBrowse research topicsOriginal source
Adaptive extended incremental Kalman filter method — Research Paper | ScholarLens