当前统计模型自适应滤波算法改进

Improved Adaptive Filter Algorithm Based on Current Statistical Model

由于"当前"统计模型自适应滤波算法对于最大加速度的过分依赖,使其对于弱机动目标并不具有较高的跟踪精度,基于"当前"统计模型自适应滤波算法的研究及目标跟踪性能的分析,提出了将目标的机动状态划分为强机动和弱机动,当目标在作弱机动运动时,可通过修正最大加速度来提高跟踪精度,分别针对常速、常加速、弱变加速三种弱机动情况进行了数学仿真,仿真结果表明,通过修正最大加速度的方法,可使该算法对于弱机动目标的跟踪精度大大提高。

Due to the ＂ current＂ statistical model adaptive filtering algorithm excessive dependence the maxi- mum acceleration, so that it does not have higher tracking precision for the weak maneuvering target. Based on the research of ＂ current＂ statistical model adaptive filtering algorithm and the analysis of the target tracking perform- ance, it is proposed that the target maneuvering state can be divided into strong maneuvering and low weak maneu- vering. When the target do weak maneuvering, a new method is proposed to improve tracking accuracy by correc- tion maximal acceleration. Through the mathematical simulation of respectively on constant velocity, constant accel- eration and weak variable accelerated. The simulation results show that through the method of correction maximum acceleration, can make filtering algorithm tracking accuracy for weak maneuvering target greatly improved.