为了有效抑制激光多普勒测速仪输出数据的随机漂移, 提高其测量精度, 在传统时序模型的基础上采用新陈代谢双时序模型进行激光多普勒测速仪漂移数据滤波.该模型由两级新陈代谢时序模型级联而成, 每一级新陈代谢时序模型均依次对13个数据点时序建模.依据此模型分别对激光多普勒测速仪静态及动态漂移数据进行建模和滤波.利用方差分析法及Allan方差法对滤波前后的测速仪静态漂移数据进行分析并利用频谱分析法对比了滤波前后的测速仪动态漂移数据.结果表明：新陈代谢双时序模型将静态漂移数据标准差减小为原始数据的44%, 将角度随机游走降为原始数据的41%; 该方法不仅能实时降低激光多普勒测速仪的静态随机漂移误差, 而且能够实时有效抑制其动态输出噪声.

To reduce the random drift of a laser Doppler velocimeter effectively and improve its measurement accuracy, a metabolic double time series model based on the traditional time series model is put forward for filtering drift data of a laser Doppler velocimeter. The model consists of a cascade of two metabolic time series models, each of which models 13 data points using a time series model in turn. The static and dynamic drift data of a laser Doppler velocimeter are modeled and filtered based on the model respectively. The variance analysis method and the Allan variance method are used to analyze the static drift data before and after being modeled and filtered. The dynamic drift data is also compared by the spectrum analysis method. The results show that this method reduces the standard deviation of the static drift data to 44% of the original data, and reduces the angular random walk to 41%. This method can not only reduce the static random drift error in real time, but can also suppress the dynamic output noise effectively.