GNSS/accelerometer integrated deformation monitoring algorithm based on sensors adaptive noise modeling

The Global Navigation Satellite System (GNSS) and accelerometer integrated system can obtain more stable and reliable deformation displacement with the full use of the complementary technical characteristics of these two types of sensors. The Kalman Filter (KF) method is often applied to integrate GNSS three-dimensional (3D) co-ordinates and high-rate accelerometer measurements. The key to obtaining accurate solutions is the reasonable setting of measurement noise and process noise of KF. However, in some complex deformation monitoring scenarios such as landslides, both GNSS and accelerometers are susceptible to interference from factors like vegetation cover and external dynamic disturbance. Traditional adaptive filtering methods is hard to simulta-neously and appropriately adjust the two types of noise matrices. To address this problem, an adaptive noise model based on sensors is proposed. The adaptive measurement noise is constructed using the posterior coor-dinate covariance derived from GNSS-RTK process. The random walk coefficient corresponding to the process noise is estimated online via the standard deviation (STD) of acceleration time-varying information. A series of numerical experiments about simulated three-dimensional (3D) deformation displacement are carried out to validate the proposed algorithm. The results show that: (1) When the GNSS signal is disturbed by complex environment, the adaptive measurement noise model effectively contributes to the accuracy improvement, from decimeter level to centimeter level. (2) In the presence of unstable deformable bodies, the adaptive process noise algorithm significantly suppresses the divergent deformation time series caused by external accelerometer dis-turbances. (3) Even if suffering the overlying effects of electromagnetic interference and external disturbance, the centimeter-level deformation monitoring accuracy can be achieved by using the joint adaptive measurement and process noise model. The improvement effect is particularly significant in the main deformation direction.