# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 2 Related Work  
In the UKF tracking algorithm, the issue of reduced filtering accuracy when the vehicle target's motion state undergoes significant changes has attracted widespread attention. Many researchers have proposed different solutions. [PERSON] et al. proposed the STJP-UKF method [10], which introduces spatiotemporal joint processing and Unscented Kalman Filter (UKF) to effectively compute the uncertain target state, improving the accuracy of state estimation. [PERSON] et al. proposed an extended target tracking (EOT) method [11], which is based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter and UKF estimator, providing obstacle state estimation based on a road-aligned curve coordinate system. [PERSON] et al. proposed an enhanced Quaternion UKF algorithm (AQUKF) [12], which improves vehicle state estimation accuracy by introducing unit quaternions to represent vehicle attitude, enabling better handling of non-Euclidean geometry. Additionally, some researchers have improved filtering accuracy by introducing adaptive weights. [PERSON] et al. proposed the adaptive M-estimate robust Unscented Kalman Filter (AMRUKF) method [13], which introduces the Huber linear regression problem and covariance matching method. It calculates adaptive matrices using an exponentially weighted innovation covariance estimator based on a decay memory approach, enhancing robustness and accuracy in vehicle collaborative positioning. [PERSON] et al. proposed the Dempster-Shafer evidence theory and improved multi-layer UKF (DS-UKF) algorithm [14], which incorporates the D-S theory into the updated weights of the sensor output values in the improved multi-layer UKF, improving positioning accuracy in intelligent connected vehicles (ICVs) at urban intersection scenarios. Some scholars have also made improvements in UKF filtering concerning strong tracking theory. For instance, [PERSON] et al. proposed a strong tracking and square root UKFalgorithm [15], which replaces the covariance matrix used in the classic UKF with the square root of the state covariance matrix to avoid filter divergence caused by negative definite state covariance matrices. Additionally, they introduced a strong tracking coefficient to adjust the filter gain in real time, improving the tracking ability for sudden state changes. [PERSON] et al. proposed an improved strong tracking UKF method [16] that reduces tracking errors by introducing topology relationship screening and environmental profile estimation to reduce motion model uncertainty. [PERSON] et al. proposed an improved Huber-M estimate and singular value decomposition-Unscented Kalman Filter (SVD-UKF)-integrated navigation algorithm [17], which introduces singular value decomposition (SVD) to optimize the prior information matrix of UKF filtering, thereby improving numerical stability. [PERSON] et al. proposed an adaptive noise matrix adjustment-based strong tracking UKF algorithm (AN-STUKF) [18], which significantly improves vehicle state estimation accuracy and tracking ability compared to traditional UKF methods. [PERSON] et al. proposed a strong tracking event-triggered non-featured Kalman filter (STETUKF) [19], which combines strong tracking filtering with ETUKF to create STETUKF, showing better state estimation performance than the traditional UKF. [PERSON] et al. proposed an Adaptive Weighted Strong Tracking Unscented Kalman Filter (ASTUKF) method to solve the problem of improving tracking accuracy for non-cooperative maneuvering target tracking [20], especially in complex dynamic environments. This method enhances system robustness and adaptability when handling maneuvering targets, achieving significant performance improvements in long-distance relative navigation. Although the existing methods have improved tracking accuracy under theoretical conditions, challenges remain in practical traffic scenarios. For example, the adjustment of adaptive factors is limited, and the precise adjustment of multidimensional system variables is difficult.