Fast Variable-Structure Multiple-Model Estimation Using Modified Likely-Model Set Tracker

Firdeh, Seyed Reza Mousavi; Karrari, M.; Menhaj, Mohammad Bagher

doi:10.1007/s40998-018-0111-3

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The model state estimator is responsible for getting the optimal state estimate in a given model. Where MSA is the critical component of VSMM, how to enhance the intelligence, flexibility, and real-time performance of the VSMM algorithm is the leading research content at present [34,35].…”

Section: Introductionmentioning

confidence: 99%

Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise

Song

Wei

et al. 2022

Sensors

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The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously estimate state and noise parameters, while its performance will also be degraded in complex noise. To address the problem of estimation accuracy degradation and result divergence of the integrated navigation system in a complex time-varying noise environment, an improved multiple-model adaptive estimation (MMAE) that combines the Sage–Husa adaptive unscented Kalman filter with the MMAE is proposed in this paper. The forgetting factor is included as an unknown parameter of MMAE so that the algorithm can adjust the value of the forgetting factor according to different system states. In addition, we improve the hypothesis testing algorithm of classical MMAE to deal with the competition problem of undesirable models that severely impacts the performance of variable-parameter MMAE and enhance the algorithm’s parameter identification capability. Simulation results show that this method enhances the system’s robustness to noises of different statistical properties and improves the estimation accuracy of the filter in time-varying noise environments.

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Section: Introductionmentioning

confidence: 99%

Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise

Song

Wei

et al. 2022

Sensors

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A Deformation Measurement Algorithm Based on Adaptive Variable Parameter Multiple Model for Large Ships

Wang

Zhang

et al. 2021

IEEE Trans. Instrum. Meas.

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Accurate measurement of ship deformation angle is of great significance for building a unified space attitude reference and improving the combat performance of the entire ship. However, many problems existing in the actual sailing process restrict its measurement accuracy severely, such as the time delay between measurement equipment, the unknown characteristics of measurement noise and the unknown model parameters of the dynamic deformation. This paper proposes an adaptive variable parameter multiple model (AVPMM) algorithm that can simultaneously solve these problems. Firstly, a time delay compensation algorithm is derived to suppress the influence of time delay on measurement accuracy; then an adaptive Cubature Kalman filter (ACKF) is designed based on the idea of adaptive filtering to suppress the influence of unknown characteristics of measurement noise; and finally an AVPMM algorithm is designed by combining the above ideas to reduce the impact of unknown model parameters of dynamic deformation. The results of simulation and turntable experiment show that this algorithm has good measurement accuracy and the error is less than 0.15 ′ .

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Fast Variable-Structure Multiple-Model Estimation Using Modified Likely-Model Set Tracker

Cited by 2 publications

References 19 publications

Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise

Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise

A Deformation Measurement Algorithm Based on Adaptive Variable Parameter Multiple Model for Large Ships

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