A Displacement Inertial Measurement System Based on Multialgorithm Fusion for Landslide

Accurate measurement of surface and subsurface landslide displacement is a fundamentally important task for landslide disaster prediction and early warning. However, the conventional landslide displacement measurement methods on the basis of point measurement cannot obtain the deformation between measurement points. The methods that can make continuous deformation available are only applicable to surface displacement measurement and their measurement capability is severely affected by environmental factors. Therefore, landslide displacement measurement applications call for an innovative method that can realize continuous deformation measurement on the surface and underground. A novel landslide displacement inertial measurement system is provided in this article which adopts low-cost fiber optic gyroscopes (FOGs) and other inertial sensors, integrates a two-dimensional N-order polynomial (TDNP) algorithm, sensor misalignment error compensation algorithm, and adaptive complementary filtering algorithm. The fusion of the multialgorithm reduces errors step by step, improves the resolution of the FOGs, calibrates the inertial sensors, and solves the problems of dead band and increased error at low angular rate for FOGs. Key architecture design points of the system are also introduced in detail. Comparison field tests with total station indicate that the system has the capability to identify cm-level landslide displacement and is feasible for landslide displacement measurement. In addition, the new method can benefit subsequent research and provide a reference for the implementation of this kind of system.