To filter out the noise and error arising out of various physical measurement processes and limitations of the acquisition technology, a Gaussian weight is assigned to each point acquired.

首先，为了去除测量产生的噪声和误差，引入高斯核函数为每个采样点加权；

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And the weight of each filter is updated using Bayes theory based on the assumption that the difference between estimate and measurement bearings obeys Gaussian distributions with zero mean error.

通过假设预测方位和实测方位差值服从零均值的高斯分布，利用贝叶斯理论来修正各滤波器的权重。

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Twice Gaussian mutation controlled the algorithm process. Inertia weight was adjusted dynamically.

该算法通过两次高斯变异控制算法进程，同时动态调节惯性权重。

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