LAUSR

To reduce the random error of the output data of a laser Doppler velocimeter (LDV) and improve its measurement accuracy, a new method to filter the drift data of the LDV is proposed: a metabolic time-series–grey model that combines a metabolic time-series model with a metabolic grey model. The basic principle is first introduced. Then, the metabolic time-series–grey model is applied to filter the drift data of an LDV and compared with the metabolic time-series model and the metabolic grey model. The variance analysis and the Allan variance are used to analyze the drift data before and after being modeled and filtered. The results show that the metabolic time-series–grey model can effectively reduce the random error of the LDV in real time and greatly improve its measurement accuracy, and its filtering effect outperforms that of the metabolic time-series model and the metabolic grey model.