Recursive Filters: SMA, EMA, Low-Pass, and a Tiny Kalman for Noisy Data
Recursive Filters: SMA, EMA, Low‑Pass, and a Tiny Kalman
I explore recursive filters like SMA, EMA, low-pass, and a simplified 1D Kalman filter for smoothing noisy data streams. These methods offer O(1) memory and compute efficiency, making them ideal for embedded systems and real-time applications. I explain the intuition behind each approach, how to tune their parameters, and when to choose one over the other for optimal performance.
Recursive filters that we'll use, all use constant-size state. That's why they're common in embedded systems, robotics control loops, mobile sensor smoothing and telemetry.