Exploring Accelerometer Noise, Gyroscope Bias, and Filter Tuning in MEMS Sensor Fusion

MEMS inertial sensors are used in drones, mobile devices, wearable technologies, and biomedical systems, yet their operating limitations can be difficult to explain solely through equations. In particular, students may treat accelerometer noise and gyroscope bias as separate concepts, without recognizing how they affect the estimated orientation over time. This study presents a browser-based virtual laboratory designed to facilitate the examination of these relationships. The laboratory simulates roll motion about a single axis and allows users to vary accelerometer noise, gyroscope bias, and the complementary filter coefficient. The true angle and estimates obtained from the accelerometer, the integrated gyroscope signal, and the fused output are displayed together as numerical values, time plots, and three-dimensional motion. Estimation performance is assessed using root mean square error, mean absolute error, mean error, and maximum absolute error. The simulator also includes a reproducible parameter sweep that tests filter coefficients from 0 to 1 under the same noise sequence. This allows students to determine how the most suitable coefficient varies with the selected noise and bias conditions, rather than treating a fixed value as universally optimal. Three guided cases address accelerometer disturbance caused by vibration, accumulated gyroscope error, and balanced sensor fusion. Students can inspect each signal, propose a fault hypothesis, compare it with the available evidence, and export the simulated data for further analysis. The model is deliberately limited to the main features needed for introductory instruction and is not presented as a complete representation of an inertial measurement unit or flight-control system. Its purpose is to provide a transparent environment in which students can relate sensor imperfections and filter settings to orientation-estimation error.