# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 5 Conclusions  
In this paper, we propose a Strong Tracking Unscented Kalman Filter algorithm based on multidimensional adaptive attenuation factors (MAST-UKF). To address the issue of decreased tracking accuracy in the traditional Strong Tracking Unscented Kalman Filter algorithm when the target's motion state undergoes significant changes, we introduced a multidimensional adaptive attenuation factor diagonal matrix before the state noise covariance and observation noise covariance to perform adaptive adjustment. The experimental results show that the MAST-UKF algorithm can effectively improve the tracking performance of targets under complex conditions by adjusting the noise covariance through the adaptive factor.  
However, due to the relatively slow adjustment of the state noise covariance by the adaptive factor introduced by MAST-UKF and the overall high computational complexity, the algorithm's convergence is somewhat limited. To further improve the algorithm's adaptability and efficiency, future research could consider optimizing the attenuation factor adjustment strategy using methods such as reinforcement learning or applying hardware acceleration techniques (such as GPU acceleration and parallel processing) to enhance filtering efficiency. Additionally, this paper only uses millimeter-wave radar (MMW Radar)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rounds** & **AST-UKF** & **MAST-UKF** \\ \hline
1 & 44.222 s & 48.285 s \\
2 & 43.572 s & 48.398 s \\
3 & 44.171 s & 48.438 s \\
4 & 43.865 s & 48.438 s \\
5 & 43.686 s & 47.876 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational time for different algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracking Algorithm** & **A X-Axis Position (m)** & **A Y-Axis Position (m)** & **B X-Axis Position (m)** & **B Y-Axis Position (m)** \\ \hline AST-UKF & 0.1970 & 1.2894 & 0.1638 & 1.1379 \\
**MAST-UKF** & **0.1836** & **0.9372** & **0.1547** & **0.8836** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Table of average velocity errors for different tracking algorithms.  
for target tracking, which limits the system's application in multi-sensor fusion. Future studies could combine MAST-UKF with other sensors, such as LiDAR, cameras, and Global Navigation Satellite Systems (GNSSs), to further reduce the errors of single sensors and improve the system's accuracy and robustness.  
Conceptualization, F.T. and S.W.; methodology, S.W.; software, S.W.; validation, F.T., S.W. and W.F.; investigation, F.T.; resources, F.T.; data curation, S.W. and T.W.; writing--original draft preparation, S.W.; writing--review and editing, F.T. and W.F.; supervision, F.T. and W.F.; visualization, F.T. and S.W.; project administration, F.T.; funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shaanxi Provincial Science and Technology Plan Project (2020 GY-029).  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.