一种新的常增益机动目标跟踪方法：α-β-γ-δ模型

New Constant Gain Algorithm for Maneuvering Target Tracking:α-β-γ-δ Model

针对目前机动目标跟踪的Jerk模型存在计算复杂度高的不足,同时受α-β-γ模型的启发,提出了一种基于Jerk模型的常增益跟踪算法:α-β-γ-δ模型,并根据时常系统的状态估计协方差在一定条件下将收敛到一个稳态值这一性质,从理论上推导出了上述新模型中α、β、γ和δ的计算公式。仿真结果表明,该算法的滤波精度比α-β-γ滤波算法高,且运算量远远小于Jerk模型算法。

Aiming at the shortcomings of the computational complexity of the Jerk for maneuvering target tracking and inspired by the α-β-γ model, a constant gain filtering algorithm proposed based on the Jerk model which is named as α-β-γ-δ model. According to the property that the state estimate covariance of the time-invariant system converge at a steady value under certain conditions, the computing formulae for α, β, γ and δ derived theoretically. Simulation results show that the accuracy of the new algorithm is higher than that ofα-β-γ filter algorithm and the computational load is also far less than that of Jerk model.