Kalman filters: The on-going “biography” of an idea - Embedded.com

Kalman filters: The on-going “biography” of an idea


One of my favorite new books and one I constantly go back to is James Gleick'snew opus titled “TheInformation.”  It is about information theory and Shannon’s contributions,among others, to understanding its implications not only to engineering butto any aspect of research into the natural world.

While it is technically rough going sometimes, what brings me backagain and again to reading it is that it is “the biography of an idea ,”as one reviewer said. While he does not spare the reader by dumbing downthe complex technical issues, Gleick is able to interweave this with theintellectual exploits and personal experiences of those who over the lastseveral hundred years have contributed to our understanding. Coincidentally,as I have been reading it, I have also been making my way through “Thenormal distribution,“ Jack Crenshaw’s most recent Insight blog on the importance of the Kalman algorithm in every aspect of electricalengineering and embedded systems design.

According to Wikipedia,(and Jack, of course ), the Kalman filter algorithm uses a series ofmeasurements observed over time, containing noise (random variations) andother inaccuracies, and produces estimates of unknown variables that tendto be more precise than those based on a single measurement alone. It operatesrecursively on streams of noisy input data to produce a statistically optimalestimate of the underlying system state and is commonly used for guidance,navigation and control of vehicles and in a wide-range of digital signalprocessing applications in wireless networks and MEMS sensor positioning.

Jack’s most recent blog  is also tough going. But rewarding. Onceyou have read it you will know that you have learned something valuable anduseful. And reading it has inspired me to create in this week's newslettera loosely-coupled biography of the Kalman algorithm consisting ofother blogs and articles on it and its use. In addition to those includedin this week’s Tech Focus Newsletter , some other related blogsinclude:

Embedded development, now and then
Observing the unknown
Sympathetic Algorithms
Look Ma, no hands!

Oops! I did it again

To add some additional substance to this “biography” of the Kalman algorithm,other articles on Embedded.com that reflect  its use in anumber of leading-edge consumer, mobile and embedded designs include:

From here to eternity
MEMS sensors: when GPS is not enough
Kalman-based dead reckoning fills vehicle GPS navigation gaps
Usingsphisticated filtering/tracing algorithms for auto safety systems

In addition, here are the links to several recent technical papers andconference presentations on its use in a range of applications:

Determine Wi-Fi signal strength for sensor location using Kalman filters
Using a cluster-based Kalman filter in wireless camera networks
Kalman-based method for motion coordination of groups of fast mobile robots
Using a sigma-point Kalman filter for alignment of a MEMS inertial measurementunit 

As these selections indicate, the usefulness of this algorithm is farfrom over. As with Gleick’s book, each article and blog I read gives me amore nuanced understanding of this powerful idea, and I would like to continuebuilding an online “biography” of this versatile algorithm.

For that I need your help with comments on the site, blogs and designarticles submitted about your experiences, as well as hearing from you aboutinteresting articles and papers you have read on this topic.

Embedded.com Site Editor Bernard Cole is also editor of thetwice-a-week Embedded.comnewsletters as well as a partner in the TechRite Associates editorialservices consultancy. He welcomes your feedback. Send an email to , or call928-525-9087.

See more articles and column like this one on Embedded.com. Signup for the Embedded.com newsletters .Copyright © 2013 UBM–All rights reserved.

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