为解决传统交互式多模型（interactive multiple model, IMM）算法在车辆目标跟踪中存在模型概率变化不明显和跟踪精度不足问题，提出一种改进的自适应IMM-UKF（unscented Kalman filter）算法。首先采用匀速直线、匀加速直线和匀速转弯来建立车辆的运动模型，并通过无迹卡尔曼滤波对车辆目标进行跟踪。然后将子模型概率变化率作为IMM算法修正参数，对马尔可夫矩阵主对角线和非主对角线元素采用不同的修正策略。最后设置判定窗修正归一化后的马尔可夫矩阵主对角线元素，以扩大匹配模型的概率。结果表明，改进算法模型概率变化更加明显，位置和速度均方根误差均要小于原有算法，有效地提高了跟踪精度。

In order to solve the problems of the traditional interactive multiple model (IMM) algorithm in vehicle target tracking, such as the model probability change is not obvious and the tracking accuracy is insufficient, an improved adaptive IMM-UKF(unscented Kalman filter) algorithm was proposed. Firstly, the vehicle motion model was established by using uniform speed straight line, uniform acceleration straight line and uniform turning, and the vehicle target was tracked by unscented Kalman filter. Then, the probability change rate of sub model was used as the correction parameter of IMM algorithm, and different correction strategies were adopted for the main diagonal and non main diagonal elements of Markov matrix. Finally, the decision window was set to modify the main diagonal element of the normalized Markov matrix to expand the probability of matching model. The results show that the probability of the improved algorithm model changes more obviously, and the root mean square errors of position and velocity are less than the original algorithm, which effectively improves the tracking accuracy.