In this article, we shall see how the Bayesian Kalman Filter can be used to predict positions of some moving particles / objects in 2D.
This article is inspired by a programming assignment from the coursera course Robotics Learning by University of Pennsylvania, where the goal was to implement a Kalman filter for ball tracking in 2D space. Some part of the problem description is taken from the assignment description.
The following equations / algorithms are going to be used to compute the Bayesian state updates for the Kalman Filter.
For the first set of experiments, a few 2D Brownian Motion like movements are simulated for a particle.
The next set of figures / animations show how the position of a moving bug is tracked using Kalman Filter.
Next the GPS dataset from the UCI Machine Learning Repository is used to get the geospatial positions of some vehicles at different times.