Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises

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 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 [email protected]

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

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

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

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

The condition was great, almost like brand new. The prime two-day shipping makes it even better. Look forward to more better deals.

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.