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Most Helpful Customer Reviews
6 of 6 people found the following review helpful:
5.0 out of 5 stars
the title says it all,
This review is from: Poor Man's Explanation of Kalman Filtering: Or How I Stopped Worrying & Learned to Love Matrix Inversion (Paperback)
I was scared that this book might be full of scary algebra, but the author has successfully described Kalman Filters in plain english with an intuitive approach. He makes it seem really simple. I recommend it.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
Great little text makes Kalman Filtering seem simple,
This review is from: Poor Man's Explanation of Kalman Filtering: Or How I Stopped Worrying & Learned to Love Matrix Inversion (Paperback)
Engineers often need to estimate a time-varying quantity or, more generally, a time-varying vector in real time. For instance, as inputs to a realtime control system you might need to estimate flow rates, temperature, position, velocity and time. It's tempting to simply read data from one sensor that measures the data of interest. What, though, if that sensor isn't accurate enough by itself or is too expensive for the application? Or what if you need multiple sensors for redundancy? How do you combine the outputs of multiple sensors when each one could provide measurements of different quantities and with different levels of precision (good, bad and ugly)? Thankfully, for those who have neither the time nor inclination to delve into the mathematics of multidimensional statistics, Dr. Kalman introduced his concept of optimum estimation in 1960, and it addresses exactly these kinds of problems in what has come to be known as the Kalman filter.
Anyone who would like to learn about Kalman filters should get a copy of this underground classic. In this paper from the late 1960s, Roger du Plessis did a tremendous favor for engineers trying to learn the ins and outs of Kalman filters. In it, he introduces these filters and compares them to the method of least squares by postulating a simple scalar estimation problem. This provides a perfect beginning because it involves two basic problems common to all Kalman filter applications: first, the need to estimate some quantity for which you know the first-and second-order statistics; second, the fact that you're measuring this quantity with an imperfect (noisy) device. Thus, this publication keeps it simple so that any engineer should be able to quickly pick up the basic idea of the Kalman filter for a fraction of what the many unhelpful books on the subject cost. If you can't get it direct from Amazon, try getting it direct from Taygeta. It is definitely worth the trouble.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
Historical Information,
By
This review is from: Poor Man's Explanation of Kalman Filtering: Or How I Stopped Worrying & Learned to Love Matrix Inversion (Paperback)
The likely predecessor of the book is a 24 page monograph, of the same title, written by Mr. du Plessis when he was Assistant Chief Engineer, Navigation Systems Division, Autonetics Division of North American Rockwell Corp. Date of the paper is June 1967.
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