在交互式多模型算法中引入神经网络算法以改进目标跟踪的精度。

The interacting multiple model(IMM) algorithm has been shown to be one of the most effective estimation algorithms in the field of maneuvering target tracking.

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该算法克服了模糊交互多模型算法精度较低和当前统计模型自适应滤波器方差调整有限的缺点。

This algorithm overcomes the weakness of the FIMM with low estimation accuracy and the limitations of the adaptive filter based on the "current"statistical model.

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该算法可以有效地限制交互多模型算法滤波计算过程中的滤波发散的可能性，从而使得IMM算法更加稳定。

The new IMM algorithm has better stability, because it can effectively restrict the like probability of divergence that happens in the IMM filter algorithm.

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