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Informationen zum Autor JOHN OLUSEGUN OGUNDARE, PHD, is a professional geomatics engineer and an instructor in the Department of Geomatics at British Columbia Institute of Technology (BCIT), Canada. He has been in the field of geomatics for over thirty years, as a surveyor in various geomatics engineering establishments in Africa and Canada and as a geomatics instructor or teaching assistant in universities and polytechnic institutions in Africa and Canada. Dr. Ogundare is also the author of Precision Surveying: The Principles and Geomatics Practice (Wiley, 2015). Klappentext PROVIDES A MODERN APPROACH TO LEAST SQUARES ESTIMATION AND DATA ANALYSIS FOR UNDERGRADUATE LAND SURVEYING AND GEOMATICS PROGRAMS Rich in theory and concepts, this comprehensive book on least square estimation and data analysis provides examples that are designed to help students extend their knowledge to solving more practical problems. The sample problems are accompanied by suggested solutions, and are challenging, yet easy enough to manually work through using simple computing devices, and chapter objectives provide an overview of the material contained in each section. Understanding Least Squares Estimation and Geomatics Data Analysis begins with an explanation of survey observables, observations, and their stochastic properties. It reviews matrix structure and construction and explains the needs for adjustment. Next, it discusses analysis and error propagation of survey observations, including the application of heuristic rule for covariance propagation. Then, the important elements of statistical distributions commonly used in geomatics are discussed. Main topics of the book include: concepts of datum definitions; the formulation and linearization of parametric, conditional and general model equations involving typical geomatics observables; geomatics problems; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre-analysis; three-dimensional geodetic network adjustment; nuisance parameter elimination and the sequential least squares adjustment; post-adjustment data analysis and reliability; the problems of datum; mathematical filtering and prediction; an introduction to least squares collocation and the kriging methods; and more. Contains ample concepts/theory and content, as well as practical and workable examples Based on the author's manual, which he developed as a complete and comprehensive book for his Adjustment of Surveying Measurements and Special Topics in Adjustments courses Provides geomatics undergraduates and geomatics professionals with required foundational knowledge An excellent companion to Precision Surveying: The Principles and Geomatics Practice Understanding Least Squares Estimation and Geomatics Data Analysis is recommended for undergraduates studying geomatics, and will benefit many readers from a variety of geomatics backgrounds, including practicing surveyors/engineers who are interested in least squares estimation and data analysis, geomatics researchers, and software developers for geomatics. Inhaltsverzeichnis Preface xiii Acknowledgments xvii About the Author xix About the Companion Website xxi 1 Introduction 1 1.1 Observables and Observations 2 1.2 Significant Digits of Observations 2 1.3 Concepts of Observation Model 4 1.4 Concepts of Stochastic Model 6 1.4.1 Random Error Properties of Observations 6 1.4.2 Standard Deviation of Observations 8 1.4.3 Mean of Weighted Observations 9 1.4.4 Precision of Observations 10 1.4.5 Accuracy of Observations 11 1.5 Needs for Adjustment 12 1.6 Introductory Matrices 16 1.6.1 Sums and Products of Matrices 18 1.6.2 Vector Representation 20 1.6.3 Basic Matrix Operations 21 1.7 ...
