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Posted: 07:42:39 PM, 04/06/2013

The biography of Kalman algorithm

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One of my favourite new books and one I frequently go back to is James Gleick's new opus titled "The Information". It is about information theory and Shannon's contributions, among others, to understanding its implications not only to engineering but to any aspect of research into the natural world.


While it is technically rough going sometimes, what brings me back again 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 down the complex technical issues, Gleick is able to interweave this with the intellectual exploits and personal experiences of those who over the last several hundred years have contributed to our understanding. Coincidentally, as I have been reading it, I have also been making my way through "Understanding the normal distribution", Jack Crenshaw's most recent Insight blog on the importance of the Kalman algorithm in every aspect of electrical engineering and embedded systems design.


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


Jack's most recent blog is also tough going. But rewarding. Once you have read it you will know that you have learned something valuable and useful. The usefulness of this algorithm is far from over. As with Gleick's book, each article and blog I read gives me a more nuanced understanding of this powerful idea, and I would like to continue building an online "biography" of this versatile algorithm.


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