Maneuvering target tracking is an important technology in engineering applications [1]-[3]. The traditional methodologies for designing it can be divided into two categories: modelbased and model-free algorithms. Almost all tracking algorithms are model based. The main idea behind modelbased tracking algorithms is to choose a representation that fits the actual state trajectories of the target movement and then to estimate the state based on the noisy observations recorded by sensors. The Kalman filter and its extensions are the most popular methods to estimate the state of a system. However, the stability and convergence rate of these algorithms depend directly on the accurate initial state estimation, unknown parameters, and covariance matrices of the process and measurement noise.

中文翻译：

---

用于机动目标跟踪的有效算法 [提示和技巧]

机动目标跟踪是工程应用中的一项重要技术[1]-[3]。传统的设计方法可以分为两类：基于模型的算法和无模型的算法。几乎所有的跟踪算法都是基于模型的。基于模型的跟踪算法背后的主要思想是选择一种适合目标运动的实际状态轨迹的表示，然后根据传感器记录的噪声观测来估计状态。卡尔曼滤波器及其扩展是最流行的估计系统状态的方法。然而，这些算法的稳定性和收敛速度直接取决于过程和测量噪声的准确初始状态估计、未知参数和协方差矩阵。