该算法采用协方差匹配技术，依据滤波新息，动态调整测量噪声方差，使融合系统的均方误差始终最小。
With covariance matching technique and innovation information of filtering, the noise variance is dynamically adjusted and the mean square error of the fusion system always keeps minimum.
与已有的融合方法相比，文章提出的神经数据融合方法具有非偏倚的统计特性而且不需要关于噪声协方差的任何先验知识。
Compared with the existing fusion method, the proposed neural data fusion method has an unbiased statistical property and does not require any prior knowledge about the noise covariance.
针对异步融合中心计算量大、实时性差的问题，基于估计协方差控制理论提出了一种多传感器异步数据融合算法。
In view of the heavier calculation burden and worse real-time of asynchronous fusion center, a multi-sensor asynchronous data fusion algorithm based on estimate covariance control was proposed.
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