John L. Junkins

Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB® codes for the examples are available on the book’s website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

Analytical Mechanics of Space Systems, Fourth Edition iterates on an already mature text by expanding some developments and discussions, as well as by including new content from spacecraft dynamics research over the last decade. It provides comprehensive treatment of dynamics of space systems, starting with the fundamentals and covering topics from basic kinematics and dynamics to more advanced celestial mechanics. Taking a tutorial approach, the text guides the reader through the various derivations and proofs to explain the principles underlying the equations at issue, and shows how to apply them to various dynamical systems. Part I covers analytical treatment of basic dynamic principles through advanced energy concepts, including use of rotating reference frames that often occur in aerospace systems. Part II covers basic celestial mechanics, treating the two-body problem, restricted three-body problem, gravity field modelling, perturbation methods, spacecraft formation flying, and orbit transfers. MATLAB, Mathematical, Python, and C-Code toolboxes are provided for rigid body kinematics routines and basic orbital 2-body orbital mechanics routines.

John L. Junkins, PhD, Distinguished Professor and Member of the NAE, has written this "professional advice" book for young and mid-career engineering professors. This book provides no-nonsense mentoring that will help professors achieve success and happiness while performing well the expected volume of teaching, research, scholarship, and winning grants. Dr. Junkins has performed these functions well for four decades, directed the research of over 50 PhD students, published 7 books, several patents and 400 other publications; he has served as Principal Investigator for $40M of research. Over 20 of his offspring are successful professors. Known as an excellent mentor, his advice is derived from first-hand experience and surveys of colleagues. He conveys many practical insights that will help you achieve a productive professional career and a happy personal life. Statistical data provided on successful associate and full professor promotions will help set goals and calibrate your performance.

Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB® codes for the examples are available on the book’s website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale dynamical system problems. It brings together a wide set of material from classical orthogonal function approximation, neural network input-output approximation, finite element methods for distributed parameter systems, and various approximation methods employed in adaptive control and learning theory.With sufficient rigor and generality, the book promotes a qualitative understanding of the development of key ideas. It facilitates a deep appreciation of the important nuances and restrictions implicit in the algorithms that affect the validity of the results produced. The text features benchmark problems throughout to offer insights and illustrate some of the computational implications. The authors provide a framework for understanding the advantages, drawbacks, and application areas of existing and new algorithms for input-output approximation. They also present novel adaptive learning algorithms that can be adjusted in real time to the various parameters of unknown mathematical models.