Research On Filtering Method Of Airborne Gravimetry Data

Gravity field information plays a significant role in resource exploration,military applications and so on.Airborne gravity is the most widely used method of measurement presently.Nevertheless,it is unavoidable to get rid of a large amount of noise in the measured data resulting from the vibration of the airframe itself and the strong dynamic influence cased by the external environment.As a result,the method that can remove data noisy effectively is of great significance.The main research contents and achievements of this paper are as follows: 1.The airborne gravity measurement data is filtered using the frequency domain filtering method,and FIR low-pass filter is designed for airborne gravity measurement.2.A cascade filtering method is proposed,which introduces Kalman filtering method based on FIR low-pass filter and processes the data of airborne gravity measurement,according to the shortcomings of FIR low-pass filter.In the case of the same resolution,the inner precision of 160 seconds lowpass filtering along with Kalman filtering is improved by 0.25 mGal compared with that of 160 seconds lowpass filtering itself.Besides,they accuracy of one is closed to the other which leads to a satisfactory result.3.The measured data is highly susceptible to the dynamic properties of the aircraft,such as the variety of flight altitude,attitude and so on,therefore,the quality of the measurement of airborne gravity data will decrease as the aircraft dynamic motion increases.A dynamic error separation method based on empirical mode decomposition is proposed pointed at the errors caused by the drastic changes in the flight height and the evidence of separation is confirmed due to correlation analysis.By the processing and analysis of the measured data,a commendable result is obtained.