Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking.

Area of Science:

• Geophysics and Structural Engineering
• Sensor Fusion and Signal Processing

Background:

• Earthquake and structural motion involve complex six-component (6C) movement (translation and rotation).
• Existing sensors cannot capture all 6C motion, and fusing data from separate instruments is challenging.

Purpose of the Study:

• To develop and validate a novel six-component (6C) Kalman filter (KF) for fusing translational and rotational motion data.
• To enhance structural and earthquake monitoring capabilities by providing more precise motion estimates.

Main Methods:

• An industrial six-axis robot arm was used to simulate structural motion, mounting various sensors.
• A six-component (6C) Kalman filter (KF) was developed to fuse data from accelerometers, Global Navigation Satellite System (GNSS) receivers, and rotational sensors.
• The KF's performance was validated against the robot's high-precision feedback system.

Main Results:

• The 6C KF successfully fused data from multiple sensors, providing accurate estimates of all six motion components.
• Integrating rotational information significantly improved the accuracy of acceleration recordings and Global Navigation Satellite System (GNSS) positions.
• Root Mean Square Error (RMSE) was substantially reduced, demonstrating the filter's effectiveness.

Conclusions:

• This study presents the first KF-based fusion of all six motion components, validated against precise ground truth.
• The proposed method effectively combines the strengths of individual instruments for superior motion estimation.
• The enhanced motion data has broad applicability in structural health monitoring and earthquake engineering.