Active Attitude Estimation Method for a Forest Harvester Head Based on Multi-Source Information Fusion

As an underactuated, heavy-load attachment mounted at the end of an external crane boom, a forest harvester head exhibits coupled active motion and passive swing, making its attitude difficult to observe directly during operation. This study considers an underactuated harvester-head system with an undamped suspension link and proposes a hydraulic–inertial multi-source information fusion cubature Kalman filter (MCKF) for monitoring the head’s active attitude and passive swing. First, a multibody dynamics model of the harvester head, including friction terms, is established from the Lagrange equations. Second, a hydraulic observation model is constructed from the fluid-continuity equations to estimate the active joint variables from pressure and flow-rate measurements. On this basis, the resulting MCKF incorporates segmented geometric-constraint mapping to combine hydraulic and dual-IMU information, thereby providing an integrated observation of active attitude and passive swing. Tilt-up and rotation tests are then conducted with the chassis and external crane boom held stationary. For the tested sequences, the mean absolute errors were 1.03° for active tilt, 0.99° for body motion expressed in the base frame, 0.679° for passive swing, and 1.60° for active rotation; the corresponding maximum absolute errors were 2.07°, 2.22°, 1.469°, and 3.84°. The results show that the complete observer can track the active and passive motion components under the tested conditions and provide a methodological basis for subsequent harvesting-head attitude monitoring and control studies.