Common Noise Forms in Accelerometer Output Signals and MATLAB Processing Methods

This document explores common noise forms present in accelerometer output signals and analyzes three prevalent signal processing techniques: classical filters, polynomial fitting, and vector Kalman filters. The classical filter approach, often implemented using MATLAB's built-in functions like filter() or designfilt(), represents the simplest method but delivers suboptimal performance. Polynomial fitting, which can be coded using polyfit() and polyval() functions, demonstrates effective low-frequency noise suppression but performs poorly against high-frequency interference. The vector Kalman filter, the most complex algorithm requiring state-space modeling and recursive estimation coding using functions like kalman(), simultaneously suppresses both low and high-frequency noise while excelling in overall signal processing performance. MATLAB simulation experiments, conducted through script-based implementations comparing filter outputs and noise reduction metrics, consistently validate the superior effectiveness of the vector Kalman filter approach.