Welcome to the home page of UNC comp 145's Kalman Filter team.

The team consisting of...

worked for... to develop an online interactive tutorial for a Kalman Filter.

The Kalman filter is a set of mathematical equations that provides an efficient computational estimate of the state of a process (e.g., the position and orientation of an airplane) given a time-varying sequence of noisy measurements (e.g., air speed, pressure, temperature, engine thrust). The filter is a popular mathematical estimator due to its efficiency and robustness. Our goal is to develop a web-based tool to help develop the intuition and insight of novice users regarding the behavior of the Kalman filter. Users would have the ability to change various input parameters and then see how the Kalman filter responds for a given set of noisy measurements. Because the software is to be primarily used as a teaching aid, we have several design goals. First, the interface should be simple, elegant, and easy to use. Input choices will be limited as necessary to avoid overwhelming the user. Parameters will be restricted to realistic but interesting values. A single, concrete physical example (e.g., sensing water level in a tank) will be used as well for all simulations. Performing multiple simulations using different physical models can lead to confusion for a novice user. Second, the output from a simulation should be organized and displayed in a manner that is easy to understand. Users should be able to see a (graphical) overview of the filter's estimation of the process state and how it compares to the actual state. Users should also be able to step through the measurement data and the equations that comprise the Kalman filter and trace the intermediate steps of the algorithm. This helps in understanding the inner workings of the Kalman filter. Finally, the resulting software should be easy to extend, modify, and maintain. The design of the software should not preclude for example the use of different physical examples or the addition of input choices and parameters.

Use The Kalman Filter On-line Learning Tool

Library of Documents

Client Meetings

Monday 10:45 - 11:30 A.M

Team Meetings

Wednesday 11:00 A.M.