Optimal Estimation of Dynamic Systems

Regular price €248.00
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
3? Bounds
3σ Bounds
A01=John L. Crassidis
A01=John L. Junkins
adaptive estimation
advanced sequential estimation theory
Aerospace Engineering
aircraft tracking
algebraic
Algebraic Riccati Equation
Attitude Error
Author_John L. Crassidis
Author_John L. Junkins
Backward Filter
bounds
Category=GPFC
Category=PBW
Discrete Time Kalman Filter
Discrete Time LQR
dynamical systems
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
equation
Era
Error Covariance
Estimation of Dynamic Systems
estimation theory
Euler Lagrange Equations
Extended Kalman Filter
filter
Fixed Interval Smoother
Fixed Lag Smoother
gaussian
Gaussian Noise Process
Gaussian White Noise Process
GPS navigation
Importance Function
kalman
Kalman Filter
Kalman filtering
Least Squares Estimation
machine control
Measurement Error Covariance Matrix
mechanical Engineering
modeling of dynamic systems
noise
numerical modeling
numerical simulation methods
Optimal Control
orbit determination
parameter identification
process
Process Noise
RBPF
riccati
Ridge Estimation
sensor fusion techniques
Sequential State Estima Tion
sequential state estimation
Smoother Estimate
spacecraft attitude determination
spacecraft navigation algorithms
Standard EKF
state space modelling
stochastic processes
Systematic Resampling
UF
white

Product details

  • ISBN 9781439839850
  • Weight: 1240g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Oct 2011
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns

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.

John L. Crassidis, Ph.D., is a professor of mechanical and aerospace engineering and the associate director of the Center for Multisource Information Fusion at the University at Buffalo, State University of New York. He previously worked at Texas A&M University, the Catholic University of America, and NASA’s Goddard Space Flight Center, where he contributed to attitude determination and control schemes for numerous spacecraft missions.

John L. Junkins, Ph.D., is a distinguished professor of aerospace engineering and the founder and director of the Center for Mechanics and Control at Texas A&M University. In addition to his historical contributions in analytical dynamics and spacecraft GNC, Dr. Junkins and his team have designed, developed, and demonstrated several new electro-optical sensing technologies.

More from this author