Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises 4th Edition by Robert Grover Brown (PDF)

Description

Advances in computers and personal navigation systems have greatly expanded the applications of Kalman filters. A Kalman filter uses information about noise and system dynamics to reduce uncertainty from noisy measurements. Common applications of Kalman filters include such fast-growing fields as autopilot systems, battery state of charge (SoC) estimation, brain-computer interface, dynamic positioning, inertial guidance systems, radar tracking, and satellite navigation systems.Brown and Hwang’s bestselling textbook introduces the theory and applications of Kalman filters for senior undergraduates and graduate students. This revision updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. The book emphasizes the application of computational software tools such as MATLAB. The companion website includes M-files to assist students in applying MATLAB to solving end-of-chapter homework problems.

User’s Reviews

Editorial Reviews: About the Author Robert Grover Brown and Patrick Y. C. Hwang are the authors of Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition, published by Wiley.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I’ve used previous editions of this book in classes, so I found the 4th ed to be a natural update of the 3rd edition. This is an excellent book for introductory material on Kalman filters, especially early chapters that lay some statistical ground work. My only complaint? I’m currently using R (not MATLAB) and I’d love to see applications in R! This is not really a problem, R package “astsa” has a number of easy-to-use R functions for Kalman filtering.. the most basic being “Kfilter0()”

⭐This book covers quite a bit, and has good examples but darn is this topic hard

⭐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 Akhtarhakhtar0027@gmail.com

⭐Shows many practical implimentation methods in Kalman filtering. Very well written with good examples and end of chapter problems. Highly recommend this book.

⭐Excellent book, very different from its 3rd edition. There are numerous new topics. Highly recommended to all: students, teachers, and practitioners.

⭐Very vauge and unclear teaching. It’s more like a review notes than an actual book.

⭐The ambition of Brown & Hwang is to provide a self-contained and pedagogical introduction to Kalman filtering, that includes the underlying stochastic process theory. This is a quite ambitious project, as both topics alone easily can fill pretty huge textbooks. I would recommend any serious student who really wants to learn this stuff to start with the first 9 chapters of Papoulis & Pillai for stochastic processes, and then move onto either the book by Gelb or (preferably) the book by Bar-Shalom, Li & Kirubarajan for Kalman filtering (One could also do Kalman first, and stochastic processes second, as I did in my PhD studies).Back to Brown & Hwang: If the objective is to learn Kalman filtering in a couple of months with only basic knowledge in statistics, I don’t think there exists any alternatives. But any student who reads this book should be aware that it lacks both depth and logical flow, and must be supplemented by other sources if basic proficiency in working with estimation is to be achieved.I will give one example of the weaknesses of the book: When introducing the concept of discretizing a continuous-time stochastic process, one needs to calculate the discrete-time Q-matrix. The authors provide an expression for Q in terms of a double integral, and then simply proceed to presenting Van Loans recipe for calculating Q without any further explanation. What they should do is instead to point out that under the standard assumption of white process noise, the double integral becomes a single integral, which actually is quite easy to evaluate, at least if we are content with a first order approximation. By neglecting this extremely important step, they make it impossible for the students to understand the connections between continuous-time and discrete-time processes, and therefore they also fail to connect the first part of the book with the second part, and the entire purpose of the book is undermined.

⭐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.

⭐Reached before timeNew bookThe book is very very useful for people working in the field of estimation and navigationThe book has new chapters as compared to 3rd version

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