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Robust Kalman Filtering Model and Its Application in GPS Monitoring Networks

LU Wei-cai

Open publisher page 3 citations

Abstract

This paper derives the Robust Kalman filtering model from the rules that the outliers in the observation vectors influence the state vectors,and this model has good robustness to observation space and design space. According to the computation results of the simulant GPS monitoring network with outliers,and compared with the results of the standard Kalman Filtering model,this Robust Kalman Filtering model can obtain reliable results of deformation analysis.

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What this paper is about

This paper derives the Robust Kalman filtering model from the rules that the outliers in the observation vectors influence the state vectors,and this model has good robustness to observation space and design space. According to the computation results of the simulant GPS monitoring network with outliers,and compared with the results of the standard Kalman Filtering model,this Robust Kalman Filtering model can obtain reliable results of deformation analysis.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper derives the Robust Kalman filtering model from the rules that the outliers in the observation vectors influence the state vectors,and this model has good robustness to observation space and design space. According to the computation results of the simulant GPS monitoring network with outliers,and compared with the results of the standard Kalman Filtering model,this Robust Kalman Filtering model can obtain reliable results of deformation analysis.

Key concepts: Kalman filter, Outlier, Robustness (evolution), Fast Kalman filter, Global Positioning System, Computer science, Computation, State-space representation

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