Linear Algebra for Localization
Algorithms, Use Cases, and C++ Implementations
1st Edition
Will be published approx. on 11. February 2026
Book
Hardback
128 pages
978-1-041-07334-5 (ISBN)
Description
Linear Algebra for Localization 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. 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
Provides clear, illustrative descriptions of the main linear algebra topics and advanced algorithms in localization problems.
C++ implementations available via a downloadable EResource at www.routledge.com/9781041073345, including detailed explanations, flowcharts, UML diagrams and text, and code runs output.
Case study by the author for an advanced topics in automotive application.
Features
Provides clear, illustrative descriptions of the main linear algebra topics and advanced algorithms in localization problems.
C++ implementations available via a downloadable EResource at www.routledge.com/9781041073345, including detailed explanations, flowcharts, UML diagrams and text, and code runs output.
Case study by the author for an advanced topics in automotive application.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Postgraduate and Undergraduate Advanced
Product notice
sewn/stitched
Cloth over boards
Illustrations
27 farbige Abbildungen, 11 s/w Zeichnungen, 27 farbige Zeichnungen, 1 s/w Tabelle, 11 s/w Abbildungen
1 Tables, black and white; 27 Line drawings, color; 11 Line drawings, black and white; 27 Illustrations, color; 11 Illustrations, black and white
Dimensions
Height: 220 mm
Width: 138 mm
Thickness: 14 mm
Weight
136 gr
ISBN-13
978-1-041-07334-5 (9781041073345)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
02/2026
1st Edition
Chapman and Hall
€73.99
Available for download
E-Book
02/2026
1st Edition
Chapman and Hall
€73.99
Available for download
Person
Ahmed Al-Muraeb is an Electrical Engineer with ~ 2 decades of multi-industrial and academic experience, with Ph.D. (Oakland University, MI, US in 2016) and M.Sc. (University of Baghdad, Baghdad, Iraq in 2004) degrees. His industrial experience spanned Cellular RF Networks operation, maintenance, system admin, and database; Cellular Value Added Services configuration, operations, and admin; and Automotive ADAS/AD features development and testing as well as hardware components validation, with 30+ certifications and trainings. Ahmed's ADAS/AD experience includes Localization solutions, since 2020 and continuing. His academic experience comprises teaching (in Michigan, US: Wayne State University, and Oakland University), and research (in Lasers and Photonics), with publications (2 dissertations, 6 journal and conference papers) and awards (8 grants and awards).
In addition to his industrial and academic achievements, Ahmed is passionate about sharing his knowledge in the clearest, most comprehensive, and accurate form; and making complex topics accessible to a broader audience. In his free time, Ahmed enjoys making art; reading; hiking; watching documentaries, movies, and shows; and listening to music.
In addition to his industrial and academic achievements, Ahmed is passionate about sharing his knowledge in the clearest, most comprehensive, and accurate form; and making complex topics accessible to a broader audience. In his free time, Ahmed enjoys making art; reading; hiking; watching documentaries, movies, and shows; and listening to music.
Content
Preface Acronyms and Abbreviations Chapter 0 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 Cramer-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 Cramer-Rao Lower Bound for Arbitrary Position Estimators