A real-time filtering method for the drift data of a laser Doppler velocimeter

ABSTRACT

To effectively reduce the random drift of a laser Doppler velocimeter (LDV), a real-time filtering model is presented for filtering the drift data of an LDV, which is a combination of the metabolic grey model (1, 1) and the metabolic time series model AR (2). The basic principle of the metabolic grey-time series model is introduced in detail first. Then, the model is established for the static and dynamic drift data, and a Kalman filter is used to filter the drift data based on the model. The variance analysis method and the Allan variance method are employed to analyse the static drift data. The dynamic drift data are also compared before and after being modelled and filtered. The results demonstrate that the metabolic grey-time series method cannot only effectively reduce the static random drift of an LDV, but can also reduce the dynamic random drift in real time.

KEYWORDS:

• Laser Doppler velocimeter
• drift data
• metabolic grey-time series model
• Kalman filter
• Allan variance

Additional information

Funding

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (grant numbers: 61308060).