Kalman filter for "dummies"

Always a good place to start. Not intimidating, intuitive beginning:

The first chapter of Peter Maybeck's book is an excellent place to start. It's free on-line. If you want to know more after reading that, I strongly recommend Peter Joseph's paper. The introduction is available on-line and he will email you the first four chapters for free. He will email you the more advanced chapter for less than $10, which is a very good deal.

Joseph's paper was much easier for me to understand than the higher (>1) chapters of Maybeck's book, but Maybeck's book is available to buy also.

Maybeck's first chapter:

I agree that the Welch-Bishop and the Maybeck sources are probably the best available.

I studied and I studied the literature and it didn't make any sense until I reread the Maybeck chapter. The most important part for me was to add two normal graphs like described by Maybeck. As soon as I did that to compute the new mean and sigma in a recursive form, then I immediately saw the functionality of the kalman filter in 1D.

So, I would say that you first understand how to add two normal distributions in batch form and then in recursive form. Maybeck's paper demonstrates that. The correlation to the multi-dimensional matrix form Kalman filter then becomes obvious.

Ed L

how is this filter applied to robotics?

Any recommended, well written, documended, Kalman filter implementations in C++ ?

It's application dependent. If you want an example of a one dimensional extended Kalman filter, look at

Mitch

On Wed, 26 Jan 2005, it was written:

Herman