Linear Algebra for Localization
Algorithms, Use Cases, and C++ Implementations
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 23 February 2026
- ISBN 9781041073345
- Binding Hardback
- No. of pages162 pages
- Size 216x138 mm
- Language English
- Illustrations 11 Illustrations, black & white; 27 Illustrations, color; 11 Line drawings, black & white; 27 Line drawings, color; 1 Tables, black & white 700
Categories
Short description:
This book emphasises the vital role of linear algebraic models in solving localization problems, as well as many other problems in algorithms, data science, and Artificial Intelligence.
MoreLong description:
This book emphasizes the vital role of linear algebraic models in solving localization problems, as well as many other problems in algorithms, data science, and artificial intelligence. Localization has multi-industrial applications, which this book attempts to address through linear algebraic approaches while using the dominant C++ programming language in those industries.
Features
This book provides clear, illustrative descriptions of the main linear algebra topics and advanced algorithms in localization problems.
It provides C++ implementations available via the associated eResource repository, including detailed explanations, flowcharts, UML diagrams and text, and code run output.
It also provides case studies by the author for advanced topics in automotive applications.
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Table of Contents:
Introduction. Chapter 1 Basic Matrix Operations. Chapter 2 Special Matrices. Chapter 3 Orthogonal Transformations. Chapter 4 Matrix Factorization. Chapter 5 Orthogonal Projections and Psudoinverse. Chapter 6 Covariance. Chapter 7 Singular Value Decomposition. Chapter 8 Jacobian, Hessian, and Gradient. Chapter 9 Fisher Information Matrix and the Cramér-Rao Lower Bound. Chapter 10 Matrix Block Operations and Matrix Kernel. Appendix A C++ Resources, Code Build, Code Run, and Code Debug. Appendix B Case Study: Effect of Reference Points Locations on Cramér-Rao Lower Bound for Arbitrary Position Estimators
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