A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization
In underwater environments, the presence of multipath effects can cause measurement outliers in acoustic sensors, leading to reduced estimation accuracy for integrated navigation. To address this issue, this paper proposes a sliding window variational Kalman filter based on Student’s <i>t</...
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MDPI AG
2025-05-01
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| author | Haoqian Huang Yutong Zhang Chenhui Dong |
| author_facet | Haoqian Huang Yutong Zhang Chenhui Dong |
| author_sort | Haoqian Huang |
| collection | DOAJ |
| description | In underwater environments, the presence of multipath effects can cause measurement outliers in acoustic sensors, leading to reduced estimation accuracy for integrated navigation. To address this issue, this paper proposes a sliding window variational Kalman filter based on Student’s <i>t</i>-distribution (SWVKF-ST) to improve state estimation accuracy. First, this method makes use of Student’s <i>t</i>-distribution to model heavy-tailed noise and adopts the inverse Wishart distribution as the prior for noise covariance, thereby enhancing robustness against heavy-tailed distributions. On this basis, the state variables and measurements within the sliding window are jointly estimated using the variational Bayesian framework, which helps mitigate the impact of unknown noise characteristics on state estimation. In addition, this method constructs multiple fading factors to prevent the degradation of estimation accuracy caused by excessive adjustment of the predicted error covariance matrix. Finally, the simulations and actual experiment validate that the SWVKF-ST outperforms the compared filters, achieving higher filtering precision and stronger robustness to outliers. The method effectively reduces the uncertainty in the measurement noise covariance matrix and demonstrates excellent adaptability in complex underwater environments. |
| format | Article |
| id | doaj-art-2baae7ba1dbb427caee92ab1f19a7785 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2baae7ba1dbb427caee92ab1f19a77852025-08-20T02:23:09ZengMDPI AGSensors1424-82202025-05-012511329110.3390/s25113291A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater LocalizationHaoqian Huang0Yutong Zhang1Chenhui Dong2College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, ChinaCollege of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, ChinaCollege of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, ChinaIn underwater environments, the presence of multipath effects can cause measurement outliers in acoustic sensors, leading to reduced estimation accuracy for integrated navigation. To address this issue, this paper proposes a sliding window variational Kalman filter based on Student’s <i>t</i>-distribution (SWVKF-ST) to improve state estimation accuracy. First, this method makes use of Student’s <i>t</i>-distribution to model heavy-tailed noise and adopts the inverse Wishart distribution as the prior for noise covariance, thereby enhancing robustness against heavy-tailed distributions. On this basis, the state variables and measurements within the sliding window are jointly estimated using the variational Bayesian framework, which helps mitigate the impact of unknown noise characteristics on state estimation. In addition, this method constructs multiple fading factors to prevent the degradation of estimation accuracy caused by excessive adjustment of the predicted error covariance matrix. Finally, the simulations and actual experiment validate that the SWVKF-ST outperforms the compared filters, achieving higher filtering precision and stronger robustness to outliers. The method effectively reduces the uncertainty in the measurement noise covariance matrix and demonstrates excellent adaptability in complex underwater environments.https://www.mdpi.com/1424-8220/25/11/3291student’s <i>t</i>-distributionvariational Bayesiansliding windowmultiple fading factorsmulti-sensor fusion |
| spellingShingle | Haoqian Huang Yutong Zhang Chenhui Dong A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization Sensors student’s <i>t</i>-distribution variational Bayesian sliding window multiple fading factors multi-sensor fusion |
| title | A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization |
| title_full | A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization |
| title_fullStr | A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization |
| title_full_unstemmed | A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization |
| title_short | A Novel Variational Bayesian Method Based on Student’s <i>t</i> Noise for Underwater Localization |
| title_sort | novel variational bayesian method based on student s i t i noise for underwater localization |
| topic | student’s <i>t</i>-distribution variational Bayesian sliding window multiple fading factors multi-sensor fusion |
| url | https://www.mdpi.com/1424-8220/25/11/3291 |
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