# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 1 Introduction  
With the rapid development of autonomous driving technology and intelligent transportation systems, traffic monitoring scenarios are becoming increasingly complex, which imposes higher demands on the target identification and tracking accuracy of millimeter-wave radar [1, 2, 3]. Currently, due to inaccuracies in system models and noise in complex traffic environments, particularly when the motion states of traffic targets undergo significant changes, millimeter-wave radar experiences a reduction in tracking accuracy during the filtering process [4, 5], which subsequently affects the precision of target tracking and traffic state evaluation. Therefore, improving the tracking accuracy of millimeter-wave radar in scenarios where vehicle target motion states undergo significant changes is of great significance for optimizing and applying intelligent transportation systems [6, 7].  
In practical applications, vehicle state estimation algorithms are crucial, and the Unscented Kalman Filter (UKF), proposed by British scholar [PERSON] et al. [8], is one of the most commonly used methods. This method achieves filtering by approximating the system's probability distribution without linearizing nonlinear functions. However, when the computational load is high, UKF can lead to a decrease in filtering accuracy. When there are outliers in the sample values, the filtering accuracy error is also significant. To address these issues, [PERSON] et al. proposed incorporating strong tracking theory intothe Unscented Kalman Filter [9]. However, in real-world complex traffic environments, traditional strong tracking UKF still faces some challenges. For example, the adjustment capability of the adaptive factor is limited, and there are difficulties in the precise adjustment of multidimensional system variables.  
The main objective of this paper is to improve the tracking accuracy of millimeter-wave radar in complex traffic environments, especially when the motion state of vehicle targets undergoes drastic changes. The proposed method enhances the performance of the Strong Tracking Unscented Kalman Filter (UKF) algorithm in such scenarios. This study introduces a multidimensional adaptive factor diagonal matrix and advanced dynamic adjustment strategies to address the limitations of current tracking methods. This approach enables more robust and accurate target tracking under complex traffic monitoring conditions.  
The main contributions of this paper are as follows:  
1. Introducing a multidimensional adaptive factor diagonal matrix solves the problem of a single adaptive factor adjustment of strong tracking UKF under multidimensional system variables and realizes the flexible adjustment in a multidimensional state.
2. Introducing the criterion of discriminating the matching degree between the actual residual covariance and the theoretical residual covariance, which makes up for the lack of practical judgment and adjustment mechanism when the noise statistical characteristics change.
3. The dynamic adjustment strategy based on UT transform solves the problem of increasing filtering error when the target motion state changes significantly.  
The remainder of this article is organized as follows. Section 2 provides an overview of the related work on Unscented Kalman Filter state estimation; Section 3 introduces the proposed algorithm; Section 4 provides experimental verification and comparative analysis; and Section 5 concludes the article.