Kalman Filter

What is Kalman Filter?

The Kalman Filter was developed by Dr. Rudolf Emil Kálmán. It is a powerful algorithm for filtering noisy data and has been widely used in various applications such as:

• Signal processing
• Control systems
• Optimization
• Sensor fusion
• System identification

The key feature of the Kalman Filter is its ability to operate in real-time (online), meaning it can process and filter incoming data as it arrives.

Key Components:

1. State Estimation: The algorithm estimates the current state of a system based on previous observations.
2. Prediction: It predicts the next state using the system model.
3. Correction: The algorithm then adjusts its prediction based on new measurements, minimizing error.

Applications:

• Tracking objects in motion (e.g., aircraft, robots)
• Smoothing noisy sensor data (e.g., GPS signals)
• Estimating parameters in dynamic systems

Experiment on Temperature Measurement

Below is an experiment using the Kalman Filter to smooth out temperature measurements from a noisy sensor.