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.

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Submission history

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

Unique-IP document downloads: 839 times

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