MATLAB Code Implementation for Calculating Weighted Acceleration

In vehicle ride comfort data analysis, we typically calculate weighted acceleration to obtain the root mean square value of weighted acceleration. It's important to note that weighted acceleration involves applying frequency-dependent weighting to acceleration signals through digital filtering techniques, providing a more accurate characterization of vehicle ride performance. During data analysis, we must account for different frequency components using appropriate weighting functions (such as Wk filters for vertical vibrations) to ensure result accuracy. Implementing this in MATLAB typically involves signal processing workflows: reading acceleration data, applying frequency weighting filters using functions like filter() or designing custom filters with fir1(), and computing RMS values using rms() or manual calculations. Alternative methods for analyzing ride comfort data include implementing digital filters (Butterworth, Chebyshev) for specific frequency bands or analyzing acceleration signal peaks using findpeaks() function. In summary, vehicle ride comfort data analysis is a complex process requiring comprehensive consideration of multiple factors, including proper filter design, frequency weighting coefficients, and statistical analysis methods to achieve optimal results.