Gravity Anomaly Extraction Method Based on Wavelet Transform and Kalman Filtering
Bingzhi Song, Shaokun Cai, Ruihang Yu, Zhiming Xiong, Bainan Yang · 2024
In standard Kalman filtering (KF), the fixed measurement noise does not accurately describe the gravity anomaly model under undulating flight conditions, leading to a decrease in measurement accuracy. To address this challenge, we propose a Kalman filtering method for gravity anomaly extraction based on wavelet transform noise estimation (WKF), which effectively tracks the measurement noise of the system. The proposed method is validated using measurement data collected under undulating flight conditions. The results show that the WKF method improves the repeatability of two undulating flight lines from 2.49 mGal and 2.63 mGal to 1.0 mGal and 2.10 mGal, respectively, and improves the repeatability of all repeat lines from 1.44 mGal to 0.96 mGal.