Integrating Moving Average Filter Windows with Analog Converter Downsampling Rates
Aligning moving average filter lengths as integer multiples of ADC decimation rates eliminates aliasing foldover and maximizes noise bandwidth reduction.
Spectral Aliasing
Signal Folding
Signal processing phenomena corrupt discrete data samples when continuous signals contain frequencies exceeding half the system sampling rate. Spectral aliasing folds high-frequency signal components into the lower baseband spectrum, creating indistinguishable false frequency components. Mathematical sampling models show that any frequency component above the Nyquist threshold mirrors across the half-sampling frequency boundary.
System qualification requires verifying front-end anti-aliasing filter rejection before analog-to-digital conversion occurs.
Nyquist Violation
Continuous signals sampled at rate F-s map all spectral energy into the fundamental Nyquist interval from zero to half F-s. Frequencies equal to F-s plus delta appear at baseband frequency delta following discrete conversion. Higher harmonics generated by non-linear sensor outputs or power line interference fold into the operational signal band if unattenuated.
Once aliasing occurs during sampling, digital signal processing algorithms cannot separate true baseband signals from folded high-frequency noise.
Filtering Suppression
Analog low-pass anti-aliasing filters placed prior to input sampling terminals suppress out-of-band signal energy. High-order active filters provide steep roll-off slopes, preserving passband flatness while attenuating signals above the Nyquist cutoff. Oversampling strategies move the effective Nyquist frequency higher, relaxing analog filter slope requirements.
Clock jitter in sampling circuits creates secondary phase aliasing, lifting the overall baseline noise floor across the entire measurement spectrum.
Measurement Error
System design bounds require anti-aliasing filter attenuation to match maximum converter dynamic range.