An Easy Explanation of Kalman Filter

The Kalman Filter for object tracking predicts the system's future state based on past measurements.

You are traveling from home to office on a beautiful Monday morning; you enter your destination as your office address into your maps apps. The app predicts traffic flow on a real-time basis to tell you the quickest path to reach the office.

Ever wondered what goes behind the scenes!

The complicated task of traffic state prediction is performed using the Kalman Filter for both stationary and non-stationary traffic conditions.

The filter is named after Rudolf E. Kálmán (May 19, 1930 — July 2, 2016). In 1960, Kálmán published his famous paper on the Kalman Filter, an optimal estimation algorithm.

The Kalman filtering is a relatively simple state-space algorithm to produce estimates of the hidden variables based on uncertain and inaccurate measurements. It predicts the systems future state based on past estimations.

Kalman Filter is applied to model systems with multiple noisy inputs and outputs hopefully less noisy and more accurately estimated output data. It is applicable to stationary and non-stationary situations.