# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 3 Multidimensional Adaptive Factor-Based Strong Tracking UKF Algorithm
### Simulation Experiments and Analysis
Overall, during the entire tracking process, MAST-UKF and AST-UKF performed excellently, especially when the target state underwent drastic changes (such as turning). MAST-UKF, with the advantage of its multidimensional adaptive attenuation factors, provided the most stable and accurate tracking results. AST-UKF followed closely behind, performing well but not as flexibly as MAST-UKF in handling complex dynamic changes. Table 2 compares the average errors of the five tracking algorithms. The results show that the MAST-UKF algorithm performs the best on both the X and Y axes of position and velocity, with the slightest error and the highest tracking accuracy; AST-UKF is the following best, while UKF has the most significant error and the worst performance. The tracking errors of MAST-UKF are reduced by 32.67%, 28.54%, and 23.17% compared to UKF, ST-UKF, and AST-UKF. Overall, the MAST-UKF algorithm significantly improves the tracking accuracy and outperforms the other algorithms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Time** & **Motion Model** \\ \hline
0–40 s & (70 m/s, 0 m/s) CV motion \\
41–75 s & Constant Turn (CT) motion with angular velocity of 0.1 rad/s \\
76–100 s & Constant Acceleration (CA) motion with acceleration of (0.1 m/s\({}^{2}\), 0 m/s\({}^{2}\)) \\
101–135 s & Constant Turn (CT) motion with angular velocity of \(-\)0.1 rad/s \\
136–175 s & (70 m/s, 0 m/s) CV motion \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Target motion model. Figure 3a shows the distance error comparison of the filtering algorithms, while Figure 3b shows the velocity error comparison. Based on the simulation results, the tracking performance of five algorithms was analyzed. Among these filtering algorithms, the MAST-UKF algorithm demonstrated the highest estimation accuracy, followed by the ST-UKF and AST-UKF algorithms with similar accuracy. In contrast, the UKF algorithm showed the lowest accuracy. In the simulation experiments, initial abnormal errors were not considered. When the target's motion state underwent significant changes, the RMSE (root mean square error) of position and velocity increased in the other four filtering algorithms, and they failed to converge quickly. The main reasons for this were mismatched motion models and the impact of measurement outliers. In comparison to other filtering algorithms, MAST-UKF achieved the best estimation accuracy and error convergence when the target underwent large-scale maneuvers. The reason for this is that the MAST-UKF algorithm proposed in this study has strong adaptive adjustment capabilities, which effectively suppress the impact of measurement and motion model errors on estimation accuracy. As a result, the MAST-UKF algorithm demonstrates excellent error adaptive convergence performance when there are significant changes in the target's motion state. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracking Algorithm** & **X-Axis Position (m)** & **Y-Axis Position (m)** & **X-Axis Velocity (m/s)** & **Y-Axis Velocity (m/s)** \\ \hline UKF & 2.3216 & 1.3404 & 1.2297 & 0.7064 \\ ST-UKF & 2.1833 & 1.2605 & 1.1925 & 0.6924 \\ AST-UKF & 2.0679 & 1.1939 & 1.1066 & 0.6396 \\
**MAST-UKF** & **1.7485** & **1.0057** & **0.7704** & **0.4456** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSEs of different tracking algorithms. Figure 2: Tracking results for each filtering algorithm.