What are good introductory books on Kalman filters? I like lots of examples and practical techniques, and less theory.
The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack.
[Reposting a comment by @Vincent-Zoonekynd from Estimate in presence of missing observations]: Here is a very simple introduction to the Kalman filter, to estimate the position of a robot (think of the position as the parameter you are trying to estimate): sites.google.com/site/udacitymirrorcs373/cs-373/unit-2 (you may want to skip part of the beginning, which is irrelevant, and check the previous and next lectures, which present non parametric alternatives to the Kalman filter: histogram filter and particle filter).
Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook by Bruce Gibbs is liberally sprinkled with examples.
One book I'm not fond of is A Kalman Filter Primer.
I did get Kalman cleared after reading 'Kalman Filter for Beginners with Matlab examples' by Phil Kim http://books.google.co.uk/books?id=W8u_XwAACAAJ&dq=kalman+filter+phil+kim&source=bl&ots=N-I0YhBX_U&sig=pcfeeEGHYmYDr7bockF5kSIMM_s&hl=en&sa=X&ei=ir5xUM3gM8Op0QWI8YDwDQ&ved=0CC4Q6AEwAA The book starts with some basic idea like recursion, moving average, low pass filter, to move to implementation of Kalman. There are Matlab's examples, which you can try by yourself and there is no space to any unclear method or derivation. The book treat Kalman Filter from practical point of view and all mathematics are left for more advanced books. Maybe after this book you will not be an expert but for sure you will know how to start to be an expert, and how to use Kalman straight away.
The book An Introduction To State Space Time Series Analysis, by Commandeur and Koopman, is small and fairly readable. It uses Ssfpack and STAMP to implement things, which made it harder for me to transfer the knowledge.