Show you how to adapt this code for
If R (measurement noise) is high, K is low → Trust the model.
Prediction:
A Kalman Filter is an optimal estimation algorithm. It estimates the true, hidden state of a system from a series of noisy measurements over time. Imagine you are tracking a drone.
Your odometer (guesses where you are based on how fast you were going). Show you how to adapt this code for
How much the actual system changes unpredictably. R (Measurement Noise): How noisy the sensor is. 5. Beyond the Basics: Extended Kalman Filter (EKF)
A Kalman filter is an optimal estimation algorithm. It combines a joint probability distribution over the variables for each timeframe to produce estimates that tend to be more accurate than those based on a single measurement alone. The Core Problem Imagine you are tracking a drone
This example demonstrates a simple Kalman filter for estimating the state of a system with a single measurement.
), you project the state forward in time. Because the real world is unpredictable, your uncertainty grows during this step. 3. Update (Measurement Update) R (Measurement Noise): How noisy the sensor is