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中文摘要：
基本的＂当前＂统计模型由于其机动加速度极限值固定不变,只能描述机动加速度较大的机动目标。因而基本的＂当前＂统计模型及其自适应滤波算法（CSAF）对机动性较强目标的预测性能较好,而对机动性较弱目标的预测误差较大。针对这个问题,新算法中设计了一种新的模糊隶属度函数,利用机动目标的＂当前＂加速度来自适应地调整机动加速度极限值,使＂当前＂统计模型可以描述具有任意加速度的机动目标。最后,运用该算法和CSAF算法对机动目标进行了航迹预测仿真实验,仿真实验结果表明,无论对于机动性较强的目标还是机动性较弱的目标,新算法的预测性能均优于CSAF算法。
英文摘要：
A basic current statistical model can only be applied to maneuvering target with high acceleration because its upper and lower limits of target acceleration is fixed and invariable. So a basic current statistical model and an adaptive filter algorithm has a good performance on maneuvering target with high accelera- tion but a poor performance on maneuvering target with low acceleration. In order to solve this problem, a novel fuzzy membership function is presented which uses the current acceleration of maneuvering target to adjust the upper and lower limits of target acceleration adaptively. This new algorithm makes sure that the current statistical model can be applied to maneuvering target with any acceleration. Finally, track predic- tion simulation is did which uses this new algorithm and CSAF algorithm to predict the track of maneuve- ring target. Simulation results show that, the prediction performance of this new algorithm is much better than CSAF algorithm on maneuvering targets both with low and high acceleration.
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