基于AIGWO-IMMUKF的目标跟踪算法

针对目标跟踪算法中滤波器选择和模型设计问题，提出了一种具有自适应性的交互式多模型无迹卡尔曼滤波（IMMUKF）目标跟踪算法。首先，介绍了IMMUKF的算法步骤；其次，提出运用改进的灰狼优化（IGWO）算法优化其中的滤波参数，通过构造调节因子建立了时变的Markov状态转移概率，形成了AIGWO-IMMUKF算法，并给出其算法流程；最后，将所提AIGWO-IMMUKF算法与传统算法在相同条件下进行仿真，得出位置、速度均方根误差曲线，以及时效性对比。结果表明，所提AIGWO-IMMUKF算法克服了传统IMMUKF算法的不足，提升了算法性能，精度和时效性都更优。 

Target tracking algorithm based on AIGWO-IMMUKF

Aimed at the problem of filter selection and model design in target tracking algorithm, an adaptive Interacting Multiple Model-Unscented Kalman Filter (IMMUKF) target tracking algorithm is proposed. First, the algorithm steps of IMMUKF are introduced. Second, the Improved Grey Wolf Optimizer (IGWO) is proposed to optimize the filter parameters, and the time-varying Markov state transition probability is established by constructing the adjustment factor. Then, the AIGWO-IMMUKF algorithm is formed and its algorithm flowchart is given. Finally, the AIGWO-IMMUKF algorithm proposed in this paper and the traditional method are simulated under the same conditions, and the root mean square error curves of position and velocity as well as the timeliness comparison chart are obtained. The results show that AIGWO-IMMUKF algorithm overcomes the shortcomings of traditional IMMUKF, improves the performance of the algorithm, and has better accuracy and timeliness.