Customer Reviews

Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only)

5 star:		(4)
4 star:		(2)
3 star:		(1)
2 star:		(2)
1 star:		(0)

 

 

This is the third edition of an introductory text on Kalman Filtering. It is easy to read, easy to follow: it presents the discrete-time and the continuous-to-discrete KFs in a clear and logical manner. The MATLAB exercises didn't seem to be integrated with the text too well, and they were written in MATALB v 4., so there are a few "corrections" that must be made ("randn" in v. 5, so all the references to "rand" in the MATLAB code must be fixed). The extended KF and some implementation issues (UDU filter, sequential estimation) are not covered as well as other topics. If you are new to the topic and are looking for a good introduction this is the book. If you already know the topic, I'd pass on the text. What I'd like to see is a new edition of Gelb, replete with MATLAB implementations..

I have read over simplified and over complicated descriptions
of the Kalman filter for years. The theoretical discussion is
well matched to the examples. THe authors have obviously had
extensive experiance TEACHING to a wide range of students and
the book benefits from their experiance.

I was able to program filters for three of the examples that
most closely match my own applications and exploit what I
learned in a matter of hours. The MATLAB code was useful, but
not critical.

I give this text 3 stars, because even though it is easy to read, it falls short of both categories that make any textbook useful: It is neither a great introductory text on the material, nor an outstanding reference for those with a good handle on estimation theory.

I used this book last semester for a graduate controls course on state estimation, and found the examples easy to read, but rather disjointed when considered as a whole. The introductory material on probability theory was incomplete, and I found that I was constantly turning to Gelb and to Mendel's "Lessons in Estimation Theory.." to get the full picture. The book also gives little treatment to modeling, which is paramount to building an effective controller.

The book does have it's merits: In addition to the typical derivation of the Kalman Filter, it provides an alternate derivation for the filter when all you have are the noisy measurements. This is a useful derivation for practical applications.

Easy to understand text dealing with Random Signals & Kalman Filtering. Also devotes an entire section to GPS. Could use optional sections offering more mathematical rigor.

I received the book in a orderly fashion, new and paperback just as described.

The book is good, but is pretty tough to read unless you have seen the material before.

I am trying to learn the subject by reading this and Gelb. Even though this book covers a lot of introductory concepts, it is lacking proper derivation of equations that I usually see in most math and physics textbooks. I find this book very disappointing for its price.

I have read over simplified and over complicated descriptions
of the Kalman filter for years. The theoretical discussion is
well matched to the examples. THe authors have obviously had
extensive experiance TEACHING to a wide range of students and
the book benefits from their experiance.

I was able to program filters for three of the examples that
most closely match my own applications and exploit what I
learned in a matter of hours. The MATLAB code was useful, but
not critical.

I use this book on a daily basis. It is worth its weight in gold.

This is not my favorite text on Kalman filters. I find there is too much emphasis on elementary, preliminary material, and not enough on application.

I teach the subject out of my own notes (draft book) where the development follows that of Gelb, Applied Optimal Estimation, and all of the computations are done in Matlab. I first read Kalman's original papers in great detail, and rederived his work before the Gelb book (MIT Press, 1974) was published. Since the advdent of Matlab (1984) I have continued to used Gelb's derivations, and augmented that work with extensive Matlab examples.

I still find Gelb more usable as a text than other, newer books, and will continue to use it along with my Matlab supplements.