一种基于自适应网格IMM的自适应采样周期算法

Adaptive sampling period algorithm based on adaptive grid IMM

提出一种自适应网格交互多模型下采样周期自适应的算法。在自适应网格交互多模型算法中,中心模型参数变化率反映了系统模型与目标机动水平间的匹配程度,而两者的匹配程度直接影响目标的跟踪精度。利用中心模型参数变化率对采样周期进行控制,并引入可控参数对平均采样周期进行灵活调整。仿真结果表明在自适应网格交互多模型下,提出的自适应采样周期算法比固定采样周期算法及基于预测协方差的自适应采样周期算法具有更好的性能。

An adaptive sampling period algorithm based on adaptive grid IMM is proposed. In the adaptive grid IMM algorithm, the changing rate of center model＇s parameter reflects the matching degree between system models and target＇s maneuvering level, which influences tracking accuracy directly. The sampling period is adjusted according to the changing rate of center model＇s parameter, and a controllable parameter is introduced to adjust the average sampling period flexibly. Simulation results demonstrate in the adaptive grid IMM algorithm, the proposed algorithm performs better than the fixed sampling period algorithm and the adaptive sampling period algorithms based on predicted covariance.