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Verification of Robustified Kalman Filters for the Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) Data,

Authors: Jeffrey W. Haak;

Verification of Robustified Kalman Filters for the Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) Data,

Abstract

Abstract : The purpose of this research is to compare the effects of two filtering routines which may be used to integrate Inertial Navigation System (INS) and Global Positioning System (GPS) data to determine certain state vector elements. The two filtering routines are: 1) the ordinary Kalman Filter, and 2) a Two-Stage Least- Squares Procedure, which will be referred to as the 2-Stage Filter. Using the Kalman Filter to determine state vector elements, the vector quantities can be affected when system error is introduced into the model. Theoretically, the 2-Stage Filter is more robust, that is, it should be able to determine accurately the state vector elements despite the presence of errors (Schaffrin 1991) This research will attempt to verify the 2-Stage Filter is, in fact, a more robust filter than the Kalman Filter.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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