Customer Reviews

Introduction to Random Signals & Applied Kalman Filtering 2e - Solutions Manual

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4 star:		(1)
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1 star:		(1)

 

 

Although this is an outdated book (there is around the 3rd edition, since 1996 Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only)), it deserves a new review because IMHO the "single star" given by a former review is quite unfair. In the end, it is a good book.

There are 10 chapters: (i) Probability and random variables; (ii) Random signals; (iii) Linear systems with random inputs; (iv) Wiener filter; (v) Discrete K filter; (vi) Apps of the discrete K filter; (vii) Continuous K filter; (viii) Discrete smoothing and prediction; (ix) Linearization of the K filter; (x) Case study: GPS.

The book lays down the foundations (random variables and processes, linear systems, Wiener filter), and builds the discrete and continuous Kalman filters theory and algorithms on top of them. It isn't focused on implementation issues (for that matter there are, for instance, G. Bierman's classic "Factorization methods for discrete sequential estimation", and more recent texts, some of them targetting real-time implementations). The treatment of the K filter follows a dual path: both frequency and time domains are used profusely. The sole critic I have is the somewhat excessive length - 500 pages. But the strong pedagogical nature is an excuse for this fact. And it deserves **** stars.

This book approaches the theory of Kalman Filtering mostly through the frequency domain approach and fails to appreciate the power of the state space dynamic representation and recursion which are the key concepts. A lot of equations but no 'real' explanations!