Kalman Filter (1-D)
This interactive simulator explores Kalman Filter (1-D) in Визуализация математики. Recursive optimal estimation: noisy measurements, hidden truth, predict + update with Q and R; random-walk or constant-velocity model with ±2σ band and innovations. Use the controls to change the scenario; watch the visualization and any graphs or readouts to connect the model with lectures, labs, and homework.
Для кого: Best once you already know the basic definitions and want to build intuition. Typical context: Визуализация математики.
Ключевые понятия
- kalman
- filter
- kalman 1d
- math
Как это работает
**1-D Kalman filter**: optimally combines a noisy measurement **z** of a hidden true signal with a **dynamic model**. Each step does **predict** (propagate state and covariance through the model **F**, growing **P** by process noise **Q**) and **update** (shrink toward the new measurement by Kalman gain **K = P_p H^T / (H P_p H^T + R)**). Crank up **R** and the filter trusts the model; crank up **Q** and it tracks the measurements aggressively. The 2-state **constant-velocity** mode lets the filter estimate velocity from position-only measurements — try the **ramp** preset to see velocity locking onto the true slope.
Основные формулы
Ещё из «Визуализация математики»
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