Data Structures and Algorithms


Kalman Folding 2: Tracking and System Dynamics

Authors: Brian Beckman

In Kalman Folding, Part 1, we present basic, static Kalman filtering as a functional fold, highlighting the unique advantages of this form for deploying test-hardened code verbatim in harsh, mission-critical environments. The examples in that paper are all static, meaning that the states of the model do not depend on the independent variable, often physical time. Here, we present a dynamic Kalman filter in the same, functional form. This filter can handle many dynamic, time-evolving applications including some tracking and navigation problems, and is easilly extended to nonlinear and non-Gaussian forms, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) respectively. Those are subjects of other papers in this Kalman-folding series. Here, we reproduce a tracking example from a well known reference, but in functional form, highlighting the advantages of that form.

Comments: 7 Pages.

Download: PDF

Submission history

[v1] 2016-06-30 20:27:15
[v2] 2016-07-06 16:57:41

Unique-IP document downloads: 1077 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus