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The Fourth�Edition to the Introduction of Random Signals and Applied Kalman Filtering is updated to cover innovations in the Kalman filter algorithm and the proliferation of Kalman filtering applications from the past decade. The text updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. Several chapters include a significant amount of new material on applications such as simultaneous localization and mapping for autonomous vehicles, inertial navigation systems and global satellite navigation systems.
- Sales Rank: #386600 in Books
- Brand: Brand: Wiley
- Published on: 2012-02-07
- Original language: English
- Number of items: 1
- Dimensions: 10.10" h x .70" w x 6.90" l, 1.55 pounds
- Binding: Hardcover
- 400 pages
- Used Book in Good Condition
Most helpful customer reviews
11 of 11 people found the following review helpful.
Progressively From Great to Just Acceptable
By Jordan McBain
When I first started to read this book, I became incredibly excited. I have parsed through a number of books on Kalman Filtering and have found that almost all of them are framed in such excessively abstract mathematical terms that the time invested was in fact wasted. The start of the book is incredibly reader friendly; statistical concepts are introduced from very simple concepts. The text then progresses to more difficult subjects like random signals and does a reasonably good job at delivering understanding. In fact, this book has one of the better discussions of statistical signal processing that I have ever seen. This largely stems from a very large number of worked examples that are of great assistance.
As the difficulty of the subject matter increases, the quality of the pedagogy decreases. The treatment of Kalman Filtering is reasonable but becomes increasingly abstract. I found myself relying heavily on the little intuition I had gained from reading other terribly written books in the field. As such, I must caution potential readers that this book is not a good introductory book. Zarchan and Musoff is the place to start in my view; once you have developed an understanding of Kalman Filtering from this perspective, Brown/Hwang will broach the more difficult aspects of kalman filtering in the context of statistical signal processing.
The need for a pedagogical text that deals with more complex considerations of kalman filtering remains.
5 of 5 people found the following review helpful.
A good treatment of the topic, but be cautious of implementation
By Text Collector
This book is a very good treatment of Kalman filtering, both for those new to the concept, as well as those looking for a deeper explanation of approaches to common practical problems including:
data lags
suboptimal modeling
computational load
sequential measurement handling
There is a section with a competent handling of a GPS standalone filter, however, the section on GPS+INS integration seems to cut many corners leaving much to be desired. For more information on sensor fusion in this specific field I recommend
1) Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems by Groves as a primary reference.
2) Aided Navigation by Jay A. Farrell as a secondary reference since it has a better treatment of coordinate frames and quaternion representations.
4 of 4 people found the following review helpful.
Intro to Kalman Filtering
By Dr.Humayun Akhtar
This book is great for an introduction to the probabilistic and stochastic pre-requisites for Kalman Filtering including the fundamental theoretical derivations and analysis of Kalman Filters and some of its extensions. I would use this book as a first book on Kalman Filtering along with Gelb's, Applied Optimal Estimation, in order to learn the fundamentals, followed by more advanced books depending upon the applications such as GNC,INS,GPS, GNSS, Radar, Finance, Econometrics, etc.
Dr.Humayun Akhtar
hakhtar0027@gmail.com
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