基于Jerk的常增益目标跟踪及其自适应算法

针对目前高机动目标跟踪的Jerk模型存在计算复杂度高和α-β-γ模型须预先估计过程噪声标准偏差的不足，提出了一种基于Jerk模型的常增益滤波算法：自适应的α-β-γ-δ模型，并从理论上推导出了上述新模型中α、β、γ和δ的计算公式。对一种典型的目标机动形式进行了Monte Carlo仿真，结果表明了新算法对于解决机动目标跟踪问题的有效性，且运算量远远小于Jerk模型算法。

Constant gain and adaptive algorithm for target tracking based on Jerk

Aiming at the shortcomings of the computational complexity of the Jerk model for highly maneuvering target tracking and the process noise standard deviation of the α-β-γ model must be estimated in advance，a constant gain filtering algorithm is proposed based on the Jerk model which is named as an adaptive α-β-γ-δ model.The computing formulae for α、β、γ and δ are derived theoretically.Finally，the Monte Carlo simulation results to a typical maneuvering movement show the validity of the new algorithm and the far less computational load than Jerk model.