Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
The tracking accuracy of the traditional Strong Tracking Unscented Kalman Filter algorithm (ST-UKF) decreases when the motion state of the traffic target changes significantly. A multidimensional adaptive factor-based strong tracking UKF (MAST-UKF) algorithm is proposed. The method introduces multid...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3276 |
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| author | Feng Tian Siyuan Wang Weibo Fu Tianyu Wei |
| author_facet | Feng Tian Siyuan Wang Weibo Fu Tianyu Wei |
| author_sort | Feng Tian |
| collection | DOAJ |
| description | The tracking accuracy of the traditional Strong Tracking Unscented Kalman Filter algorithm (ST-UKF) decreases when the motion state of the traffic target changes significantly. A multidimensional adaptive factor-based strong tracking UKF (MAST-UKF) algorithm is proposed. The method introduces multidimensional attenuation factors in the prediction and updating process of filtering, and realizes the strong tracking filtering of vehicle targets by adjusting the uncertainty of state noise covariance and observation noise covariance and dynamically updating the multidimensional attenuation factors by adaptively adjusting the threshold based on the observation residuals and the state estimation error. Target tracking simulations are performed under system model uncertainty, and the tracking errors of MAST-UKF are reduced by 32.67%, 28.54%, and 23.17% compared to UKF, ST-UKF, and AST-UKF, respectively. The real vehicle experiments show that MAST-UKF reduces the distance error by 18.29% and speed error by 15.25% compared to AST-UKF. The results demonstrate that the MAST-UKF algorithm is able to adaptively adjust the noise covariance and effectively cope with the inaccuracy of the state noise and observation noise, thus realizing the accurate tracking of the target under complex conditions. |
| format | Article |
| id | doaj-art-a9dbf33aad0d4750a02dcad8b0b77e2a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-a9dbf33aad0d4750a02dcad8b0b77e2a2025-08-20T02:11:12ZengMDPI AGApplied Sciences2076-34172025-03-01156327610.3390/app15063276Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman FilterFeng Tian0Siyuan Wang1Weibo Fu2Tianyu Wei3College of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, ChinaThe tracking accuracy of the traditional Strong Tracking Unscented Kalman Filter algorithm (ST-UKF) decreases when the motion state of the traffic target changes significantly. A multidimensional adaptive factor-based strong tracking UKF (MAST-UKF) algorithm is proposed. The method introduces multidimensional attenuation factors in the prediction and updating process of filtering, and realizes the strong tracking filtering of vehicle targets by adjusting the uncertainty of state noise covariance and observation noise covariance and dynamically updating the multidimensional attenuation factors by adaptively adjusting the threshold based on the observation residuals and the state estimation error. Target tracking simulations are performed under system model uncertainty, and the tracking errors of MAST-UKF are reduced by 32.67%, 28.54%, and 23.17% compared to UKF, ST-UKF, and AST-UKF, respectively. The real vehicle experiments show that MAST-UKF reduces the distance error by 18.29% and speed error by 15.25% compared to AST-UKF. The results demonstrate that the MAST-UKF algorithm is able to adaptively adjust the noise covariance and effectively cope with the inaccuracy of the state noise and observation noise, thus realizing the accurate tracking of the target under complex conditions.https://www.mdpi.com/2076-3417/15/6/3276millimeter-wave radarstrong tracking UKFadaptivetarget tracking |
| spellingShingle | Feng Tian Siyuan Wang Weibo Fu Tianyu Wei Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter Applied Sciences millimeter-wave radar strong tracking UKF adaptive target tracking |
| title | Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter |
| title_full | Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter |
| title_fullStr | Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter |
| title_full_unstemmed | Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter |
| title_short | Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter |
| title_sort | vehicle target tracking algorithm based on improved strong tracking unscented kalman filter |
| topic | millimeter-wave radar strong tracking UKF adaptive target tracking |
| url | https://www.mdpi.com/2076-3417/15/6/3276 |
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