A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints
In this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/3809689 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849305011576635392 |
|---|---|
| author | Tianli Ma Rong Zhang Song Gao Hong Li Yang Zhang |
| author_facet | Tianli Ma Rong Zhang Song Gao Hong Li Yang Zhang |
| author_sort | Tianli Ma |
| collection | DOAJ |
| description | In this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. To achieve the inequality-constrained estimation, the constrained state is computed by truncating the probability density function (PDF) of the estimated state after the variational update stage; the mean and covariance of the constrained state are the first and second moments of the truncated PDF. Considering the model uncertainties where the system dynamics are unpredictable, a multiple model VB truncated adaptive filter is proposed in the interacting multiple model framework. The performances of the proposed algorithms are evaluated via the target tracking simulations and the robot positioning experiments. Results show that the proposed algorithms improve estimation accuracy compared with the existing adaptive filters when the states suffer inequality constraints. |
| format | Article |
| id | doaj-art-40c2ea1b83b349e9beb61ffdcc1a6294 |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-40c2ea1b83b349e9beb61ffdcc1a62942025-08-20T03:55:35ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/3809689A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality ConstraintsTianli Ma0Rong Zhang1Song Gao2Hong Li3Yang Zhang4College of Electronics Information EngineeringCollege of Electronics Information EngineeringCollege of Electronics Information EngineeringCollege of Electronics Information EngineeringThe 36th Research Institute of China Electronics Technology Group CorporationIn this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. To achieve the inequality-constrained estimation, the constrained state is computed by truncating the probability density function (PDF) of the estimated state after the variational update stage; the mean and covariance of the constrained state are the first and second moments of the truncated PDF. Considering the model uncertainties where the system dynamics are unpredictable, a multiple model VB truncated adaptive filter is proposed in the interacting multiple model framework. The performances of the proposed algorithms are evaluated via the target tracking simulations and the robot positioning experiments. Results show that the proposed algorithms improve estimation accuracy compared with the existing adaptive filters when the states suffer inequality constraints.http://dx.doi.org/10.1049/2024/3809689 |
| spellingShingle | Tianli Ma Rong Zhang Song Gao Hong Li Yang Zhang A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints IET Signal Processing |
| title | A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints |
| title_full | A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints |
| title_fullStr | A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints |
| title_full_unstemmed | A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints |
| title_short | A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints |
| title_sort | variational bayesian truncated adaptive filter for uncertain systems with inequality constraints |
| url | http://dx.doi.org/10.1049/2024/3809689 |
| work_keys_str_mv | AT tianlima avariationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT rongzhang avariationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT songgao avariationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT hongli avariationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT yangzhang avariationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT tianlima variationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT rongzhang variationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT songgao variationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT hongli variationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints AT yangzhang variationalbayesiantruncatedadaptivefilterforuncertainsystemswithinequalityconstraints |