Risk sensitive Kalman filter with non-scalar risk-sensitive factor

Acharya, Arunasish; Sadhu, Smita; Ghoshal, Tapan Kumar

doi:10.1109/indcon.2010.5712613

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…In this paper, we apply the nonlinear model [4] with square-root risk-sensitive unscented Kalman filter (SR-RSUKF) to compensate random error. The filter which is based on the risk-sensitive unscented Kalman filter (RSUKF) [7] has the following characters: being applicable to models with non-additive noise items, employing non-scalar risk sensitive factor [8] and applying square root algorithm which is used in the square-root unscented Kalman filter (SR-UKF) [9].…”

Section: Introductionmentioning

confidence: 99%

Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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In order to compensate MEMS gyroscope random error, a new method employing the squareroot risk-sensitive unscented Kalman filter (SR-RSUKF) and a nonlinear model is proposed. The nonlinear model based on ARIMA takes model parameters as states, and thus realizes the online model estimation. The SR-RSUKF deals with non-additive noise items through augmented state vector, and employs a square root algorithm to get well numerical stability, and improves the flexibility by extending scalar risk parameters to a risk sensitive matrix. In experiments, the raw sample data is processed with three methods using different models and filters. And the results show that the SR-RSUKF together with the nonlinear model provide a competent solution to compensate MEMS gyroscope random error.

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Section: Introductionmentioning

confidence: 99%

Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

View full text Add to dashboard Cite

show abstract

Risk sensitive Kalman filter with non-scalar risk-sensitive factor

Cited by 1 publication

References 9 publications

Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter

Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter

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