Indoor Height Estimation Method for Scissor Lift Based on Sensor Information Fusion

Accurate height information is crucial for enhancing the safety and operability of scissor lift in indoor operations. To address issues such as outlier interference, integral drift, and barometric pressure perturbation in existing single-sensor height measurement methods, this paper proposes a height estimation algorithm that fuses accelerometer and barometer data. The dynamic threshold outlier rejection mechanism is developed using jerk analysis combined with the improved Interquartile Range (IQR) outlier detection algorithm, reducing high-frequency noise interference in the accelerometer. The Finite State Machine (FSM)-based adaptive Kalman filter identifies the motion state and dynamically adjusts drift compensation, enhancing the algorithm's stability. An Extended Kalman Filtering (EKF) fusion model is developed to construct an observation equation using the barometer's absolute height measurement and the accelerometer's relative displacement. The height estimation error is kept within 0.14mby leveraging the complementary characteristics of both sensors, providing a high-precision height reference for scissor lift in indoor operations. This method has broad application potential in indoor positioning and navigation.