Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises
| Author | : | |
| Rating | : | 4.25 (873 Votes) |
| Asin | : | 0470609699 |
| Format Type | : | paperback |
| Number of Pages | : | 400 Pages |
| Publish Date | : | 2016-05-19 |
| Language | : | English |
DESCRIPTION:
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.. 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
"Progressively From Great to Just Acceptable" according to 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. "A good treatment of the topic, but be cautious of implementation" according to 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 lagssuboptimal modelingcomputational loadsequential measurement handlingThere 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 t. Intro to Kalman Filtering 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 suc