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针对再入目标跟踪问题,基于加速度动力学模型和随机模型近似思想,提出了分段匀Jerk自适应模型及跟踪算法.该算法引入Jerk动力学模型和Jerk分段均匀假设,给出了机动加速度的递推模型;根据随机模型近似思想提出了新的过程噪声定义方法并给出了分段匀Jerk模型和过程噪声的自适应方法;结合状态扩展方法和分离差分滤波算法实现了再入目标的实时自适应跟踪.仿真实验表明,相比基于分段匀加速模型的跟踪算法,该算法在保证了再入目标稳态跟踪精度的同时,对目标突变状态具有较强的跟踪能力.
Aiming at the re-entry target tracking problem, based on the acceleration dynamics model and the stochastic model approximation idea, a piecewise uniform Jerk adaptive model and a tracking algorithm are proposed.The Jerk dynamic model and the Jerk segment uniform assumption are introduced, and the maneuvering acceleration A new method for defining process noise is proposed based on the approximation of stochastic models. An adaptive method for piecewise uniform Jerk model and process noise is also given. Combining with the state extension method and the separation of differential filtering algorithm, Real-time adaptive tracking shows that compared with the tracking algorithm based on the uniform acceleration model, the proposed algorithm can keep track of the target sudden change state while ensuring the steady-state tracking accuracy of the reentry target.