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Trajectory optimization. --- Aerodynamics. --- Fire control (Gunnery)
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Trajectory optimization. --- Aerodynamics. --- Fire control (Gunnery)
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This text covers fundamentals used in the navigation and guidance of modern aerospace vehicles, in both atmospheric and space flight. It can be used as a textbook supporting a graduate level course on aerospace navigation and guidance, a guide for self-study, or a resource for practicing engineers and researchers. It begins with an introduction that discusses why navigation and guidance ought to be considered together and delineates the class of systems of interest in navigation and guidance. The book then presents the necessary fundamentals in deterministic and stochastic systems theory and applies them to navigation. Next, the book treats optimization and optimal control for application in optimal guidance. In the final chapter, the book introduces problems where two competing controls exercise authority over a system, leading to differential games. Fundamentals of Aerospace Navigation and Guidance features examples illustrating concepts and homework problems at the end of all chapters.
Navigation (Astronautics) --- Space vehicles. --- Trajectory optimization. --- Space flight.
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Dynamical systems. --- Trajectory optimization. --- Boundary conditions. --- Trajectories. --- Computational grids. --- Nonlinear programming.
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Astrodynamics. --- Interplanetary trajectories. --- Launch windows. --- Trajectory optimization. --- Mission planning. --- Vector analysis.
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Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.
Computer vision. --- Trajectory optimization. --- Optimization, Trajectory --- Aerodynamics --- Space trajectories --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment
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