基于改进当前统计模型的自适应无源跟踪算法

针对无源跟踪中，标准当前统计模型无法自适应调整加速度极限值的缺点，设计了一种修正系数来通过机动目标的当前加速度自适应调整模型的加速度极限值，同时利用模糊控制的方法对修正系数的取值进行实时调整，实现了对当前统计模型的改进。最后结合容积卡尔曼滤波算法构造基于改进当前统计模型的自适应无源跟踪算法。仿真结果表明，相比基于标准当前统计模型的自适应跟踪算法，新算法对非机动目标、弱机动目标以及强机动目标都有更好的跟踪效果。

Adaptive algorithm based on modified current statistical model for passive tracking

Aiming at the defect that normal current statistical model can not adjust the limits of target acceleration adaptively in passive tracking, a correctional coefficient is designed, through the current acceleration of maneuvering targets to adjust the limits of target acceleration adaptively. Meanwhile, with fuzzy control, the correctional coefficient is adjusted in real-time, then the model is improved. Finally, this improved model is combined with a Cubature Kalman Filter（CKF）to form the modified current statistic model for the passive tracking algorithm. Simulation results show that, compared with the adaptive tracking algorithm based on normal current statistical model, the new algorithm has better performance on tracking non-maneuvering and weak and strong maneuvering targets.