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Adaptive unscented Kalman filter in Inertial Navigation System alignment

Xianlin Huang, Zhenkai Wang

Open publisher page 10 citations

Abstract

An adaptive unscented Kalman filter is presented, in this algorithm, modified Sage-Husa noise statistics estimator is introduced to estimate the process noise variances adaptively. The nonlinear filter is applied in INS alignment with large azimuth error. Simulation results show that adaptive Unscented Kalman filter performance well in the special case.

About this research paper

What this paper is about

An adaptive unscented Kalman filter is presented, in this algorithm, modified Sage-Husa noise statistics estimator is introduced to estimate the process noise variances adaptively. The nonlinear filter is applied in INS alignment with large azimuth error. Simulation results show that adaptive Unscented Kalman filter performance well in the special case.

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OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

An adaptive unscented Kalman filter is presented, in this algorithm, modified Sage-Husa noise statistics estimator is introduced to estimate the process noise variances adaptively. The nonlinear filter is applied in INS alignment with large azimuth error. Simulation results show that adaptive Unscented Kalman filter performance well in the special case.

Key concepts: Kalman filter, Unscented transform, Control theory (sociology), Computer science, Fast Kalman filter, Extended Kalman filter, Invariant extended Kalman filter, Inertial navigation system

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