Best Reference Books – Estimation and Identification

We have compiled the list of Top 10 Best Reference Books on Estimation and Identification subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Estimation and Identification along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Estimation and Identification subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Estimation and Identification" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Optimal Filtering” by B D O Anderson and J B Moore

Book Review: This book contains information on signal processing thereby dealing with communication systems and digital filtering theory. This is very good textbook for students in the field of control and communications, statistics, economics, bioengineering and operations research. The topics covered in the book include filtering, linear systems, kalman filter, time invariant filters, discrete time signal smoothing, nonlinear filtering, innovation representation, special factorization, weiner and levinson filtering, parameter identification and adaptive estimation.

2. “Lectures on Wiener and Kalman filtering” by T Kailath

“Lectures on Wiener and Kalman Filtering” Book Review: This book is a series of lecture notes on Wiener and Kalman filtering intended for students and professionals. An introduction to least-square estimates and its fundamental properties is followed by an in-depth discussion of Wiener filters and its associated techniques and generalizations. Some examples of state-space models are also added. The text then covers Kalman filters, continuous-time Kalman filters, and discrete-time recursive estimation. Separate chapters are dedicated to recursive Wiener filters, fast algorithms for constant parameter models, and relation of Kalman filter to Wiener filter. Examples, problems, and appendices are provided for a better understanding of the subject.

3. “System Identification Theory for the User” by L Ljung

Book Review: Appropriate for courses in System Identification. This book is a comprehensive and coherent description of the theory, methodology and practice of System Identification-the science of building mathematical models of dynamic systems by observing input/output data. It puts the user in focus, giving the necessary background to understand theoretical foundation and emphasizing the practical aspects of the options and choices that face the user. The Second Edition has been updated to include material on subspace methods, non-linear black box models-such as neural networks-and methods that use frequency domain data.

4. “Stochastic Models, Estimation and Control Vol.I” by P S Maybeck

“Stochastic Models, Estimation and Control: Volume 1” Book Review: This self-contained book aims to develop a complete understanding of the fundamentals of stochastic processes, estimation, and control. Both the mathematical results as well as implementation of estimation and control algorithms in this field are included. Spanning over seven chapters, the book covers deterministic system models, stochastic processes and linear dynamic system models, optimal filtering and linear system models, probability theory and static models, design and performance analysis of Kalman filters, and square root filtering. Numerous mathematical proofs and examples are also provided. This book is intended for engineers as well as students and researchers studying in engineering courses.

5. “Stochastic Models, Estimation and Control Vol.II” by P S Maybeck

“Stochastic Models, Estimation and Control: Volume 2” Book Review: This book is built upon the foundations set in Volume 1 and explains optimal smoothing in addition to filtering and compensation of linear model inadequacies. The book also develops nonlinear stochastic system models to help elaborate the design of practical nonlinear estimation algorithms. An initial study of the important extended Kalman filter algorithm is also discussed. A description of adaptive estimation based upon linear models where uncertain parameters are embedded is also included. Numerous mathematical proofs and examples are also provided. Prerequisite knowledge of advanced calculus, differential equations, basic vector and matrix analysis on an engineering level is required. This book is intended for engineers as well as students and researchers studying in engineering courses.

6. “Stochastic Models, Estimation and Control Vol.III” by P S Maybeck

“Stochastic Models, Estimation and Control: Volume 3” Book Review: This book is a continuation of the Volumes 1 and 2 of the same series. The fundamentals of stochastic control and dynamic programming are introduced as a means of synthesizing optimal stochastic control laws. This is followed by LQG synthesis of controllers based upon linear system models, Gaussian noise models, and quadratic cost criteria for defining optimality. The book also develops practical nonlinear controllers using the concepts explained in Volumes 1 and 2. Numerous illustrations and examples are also provided. Prerequisite knowledge of advanced calculus, differential equations, basic vector and matrix analysis on an engineering level is required. This book is intended for engineers as well as students and researchers studying in engineering courses.

7. “Random Signals Estimation and Identification: Analysis and Applications” by Nirode Mohanty

“Random Signals Estimation and Identification: Analysis and Applications” Book Review: This book presents a comprehensive introduction to random signal analysis, estimation, filtering, and identification by focusing on its computational aspects. Com­mon analytical tools for systems involving random signals are also covered in detail. Essential topics covered in this book include random processes, stationary signals, estimation, spectrum estimation, spectral analysis, filtering, optimiz­ation, detection, prediction, and identification. The algorithms presented in this book can also be successfully applied to industrial projects. Exposure to basic probability and linear algebra is assumed. The book is ideal for practicing engineers, scientists, undergraduate and graduate students studying random processes, estimation theory, and system identification.

8. “Spectrum Estimation and System Identification” by Theodore I Shim S Unnikrishna Pillai Shim Pillai

“Spectrum Estimation and System Identification” Book Review: This book presents a new outlook to spectrum estimation and system identification with the help of positive functions and bounded functions. These powerful concepts have been indispensable in analyzing various signal processing problems, interpolation problems, and the system identification problem from a different perspective. Rational and stable approximation of non-rational transfer functions in the single-channel case and the multichannel case are examined for its applications in distributed system theory, analysis of differential equations with delays, and simulation of systems governed by partial differential equations. This book is suitable for engineers, researchers, and graduate level students.

9. “State Estimation Techniques: Identification of Parameters of Finite Element Models” by Ahmed Nasrellah Hassan Dr

“State Estimation Techniques: Identification of Parameters of Finite Element Methods” Book Review: This book reviews the development and application of some dynamic state estimation based methods for estimation of parameters of vibrating structures. The text introduces a pseudo-time parameter to investigate strategies for data fusion from multiple tests of different types and quantities of sensor within the framework of Kalman and Particle filtering techniques. A discussion on using finite element models in commercially available softwares to communicate with databases of measurements via particle filtering algorithm developed on MATLAB is also included. The proposed methods are applied to finite element model updating of bridge structures and vehicle structure interaction problems.

10. “Recursive Identification and Parameter Estimation” by Chen Zhao

“Recursive Identification and Parameter Estimation” Book Review: This book solves system identification and parameter estimation problems arising from diverse areas using a recursive approach. The book starts with an introduction to crucial topics such as probability theory, martingales, martingale difference sequences, Markov chains, mixing processes, and stationary processes. It then addresses the root-seeking problem for functions solved using the classic RM algorithm and SAAWET. Readers can also learn to recursively identify ARMAX systems as well as nonlinear systems. Theoretical analysis and proposed algorithms are provided to help develop modeling and identification skills to successful research and application. The book is designed for students, researchers, and engineers working in systems and control, signal processing, communication, and mathematical statistics.

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