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A new parameters adaptively adjusting method of current statistical model

机译:当前统计模型参数自适应调整的新方法

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摘要

The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function to adaptively adjust maximum acceleration and maneuvering frequency is proposed based on current statistical model. Although the new model has a better performance, a fluctuant phenomenon appears. As far as this problem is concerned, a new filter algorithm which is based on amendatory and adaptively fading kalman filtering is proposed. The results of simulation indicate the effectiveness and coherent of the new model and the new algorithm, and their well performance in maneuvering target tracking.
机译:当前统计模型的固定最大加速度和操纵频率导致滤波算法的分歧。在这项研究中,基于当前的统计模型,提出了一种新的模型,该模型采用创新支配的隶属函数来自适应地调整最大加速度和操纵频率。尽管新模型具有更好的性能,但出现了波动现象。针对这一问题,提出了一种基于修正和自适应衰落卡尔曼滤波的滤波算法。仿真结果表明了新模型和新算法的有效性和连贯性,以及它们在机动目标跟踪中的良好性能。

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