Signal Processing
Sampling, Fourier, filtering — taming noisy signals.
mediumSensing & Signals
Why it matters in robotics
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Application focus
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At a glance
Typical sensor-to-data signal chain: an anti-aliasing filter must come before sampling, and digital filtering/transforms happen after.
What to study
- ✓Sampling theory: the Nyquist-Shannon theorem, aliasing/frequency folding, why anti-aliasing filters go BEFORE the ADC, and how to choose a sample rate.
- ✓Frequency domain: what the DFT/FFT computes, reading a magnitude spectrum, frequency resolution vs. window length, and the time-frequency (uncertainty) trade-off.
- ✓Convolution and linear filters: how convolution implements filtering, impulse/frequency response, and the behavior of low-, high-, and band-pass filters.
- ✓Practical digital filter design and noise: FIR vs. IIR (e.g., Butterworth), phase lag/group delay vs. smoothing, SNR, and cutoff selection for real sensor signals.
Study by time budget
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