Keywords: lateral acceleration, inertial measurement unit, global navigation satellite system, gyroscope heading, decimation filtering, railway monitoring, digital signal processing.

This paper presents a comparative analysis of lateral acceleration estimation in railway environments using three information sources: measurements from a triaxial accelerometer and heading information from the gyroscope of an Inertial Measurement Unit (IMU), and kinematic estimations derived from data from a Global Navigation Satellite System (GNSS). The proposed methodology incorporates a decimation and filtering procedure to synchronize high-frequency IMU data streams with lower-frequency GNSS measurements, thus establishing a common spatial and temporal reference framework. This study validates the framework using an open-access experimental railway dataset. The results confirm the physical consistency of all three estimation strategies and characterize their respective sensitivities to track curvature, geometric irregularities, and sensor noise. In general, the study provides a transparent, physics-based alternative to black-box machine-learning approaches for railway diagnostics.

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