Application Of Statistical Filter Theory To Optimal Estimation Of Position And Velocity On Board Circumlunar Vehicle With List Of References
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Author | : Gerald L. Smith |
Publisher | : |
Total Pages | : 40 |
Release | : 1962 |
Genre | : Space trajectories |
ISBN | : |
Statistical filter theory is employed to develop a method for determining the best possible estimate of the position and velocity of a space vehicle in the midcourse phase of flight. Results of a computer simulation are given to illustrate the performance attainable. An onboard system is visualized in which the source of information is an arbitrary sequence of observations of space angles, corrupted by measurement errors. The scheme is in effect a dynamical time-varying filter, implemented by a digital computer, which processes the incoming data to compute an up-to-date optimal estimate of position and velocity.
Author | : |
Publisher | : |
Total Pages | : 27 |
Release | : 1962 |
Genre | : Space trajectories |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 1990 |
Release | : 1962 |
Genre | : Government publications |
ISBN | : |
Author | : Gerald L. Smith |
Publisher | : |
Total Pages | : 27 |
Release | : 1962 |
Genre | : Space trajectories |
ISBN | : |
Author | : United States. Superintendent of Documents |
Publisher | : |
Total Pages | : 1248 |
Release | : 1961 |
Genre | : Government publications |
ISBN | : |
Author | : United States. Superintendent of Documents |
Publisher | : |
Total Pages | : 1250 |
Release | : 1979 |
Genre | : United States |
ISBN | : |
Author | : Mykel J. Kochenderfer |
Publisher | : MIT Press |
Total Pages | : 350 |
Release | : 2015-07-24 |
Genre | : Computers |
ISBN | : 0262331713 |
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Author | : Christian Lundquist |
Publisher | : |
Total Pages | : 280 |
Release | : 2015-04-02 |
Genre | : |
ISBN | : 9789144100111 |
Author | : John D. McLean |
Publisher | : |
Total Pages | : 66 |
Release | : 1962 |
Genre | : Aerodynamics |
ISBN | : |
Author | : Branko Ristic |
Publisher | : Artech House |
Total Pages | : 328 |
Release | : 2003-12-01 |
Genre | : Technology & Engineering |
ISBN | : 9781580538510 |
For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.