Two Filter Methods Application In The Airborne Gravity Anomaly Solution

Airborne gravimetry based on aircraft measures gravity of near-Earth gravity.However,gravity anomalies are often submerged in its carrier acceleration which is more a few times to several hundred times than the gravity anomalies,in order to be able to extract the useful gravity anomaly,you need to do further research.Based on GT-1A airborne gravimetry system,some reaseracher process the airborne gravity data with the conventional Kalman filter.However the conventional Kalman has some problems itself,so the square-root filter and the fuzzy logic Kalman filter have been intruduced to process the vertical acceleration correction,and achieve good results.The main research work and achievements are as follows:First,the calculate divergence problem of conventional Kalman filter has been studied.By constructing a constant velocity model,the conventional Kalman filter result is divergence for pathological matrix,because of the accumulation of computer rounding errors,resulting in divergence of the filtering result.But after using the square-root filter the divergence can be effectively suppressedSecondly,the conventional Kalman filter algorithm needs exactly know the information of the system.By building a model experiment,through the filtering criteria,it has been found that if the system noise and measurement noise statistics information is larger or smaller than the true value,the use of fuzzy logic adaptive Kalman filter has a higher accuracy than the conventional Kalman filter.Finally,applying the square-root filter and the fuzzy logic adaptive Kalman filter instead of the conventional Kalman filter in the airborne gravity solution.We can found the square-root filter has the same accuracy with the conventional Kalman filter and the fuzzy logic system noise adaptive Kalman filtering and the fuzzy logic measurement noise adaptive Kalman filter can improve the accuracy.