Multidimensional Signal Processing, Volume 54 in the Handbook of Statistics series is dedicated to presenting the latest developments and methodologies in multidimensional signal processing. The book aims to provide a comprehensive overview of the theories, models, and methods that form the foundation of this field. Chapters in this new release include Robust Parameter Estimation of Two Dimensional Chirp Model, Computability Theory for Multidimensional Signal Processing, Tensor signal processing, Spectral compressed sensing by structured matrix optimization methods, Space-time imaging, Hypercomplex Widely Linear Processing, and much more. The book's chapters are meticulously curated to offer detailed, educational content rather than conventional journal-style articles.
Other chapters cover Hypercomplex phase retrieval, Hypercomplex widely linear estimation, MIMO radar signal processing, Computational lidar, Signal processing applications of higher-dimensional graphs, Space-Time Radio Signal Processing by Photonic Upconversion, Computational imaging, and Topology identification and learning over graphs using multi-dimensional data.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1: Robust parameter estimation of two dimensional chirp model - p. 1 9. Chapter 2: Computability theory for multidimensional signal processing - p. 33 10. Chapter 3: Generalized decompositions for tensor data - p. 67 11. Chapter 4: Spectral compressed sensing via structured matrix optimization - p. 91 12. Chapter 5: Space-time imaging - p. 119 13. Chapter 6: Hypercomplex phase retrieval - p. 147 14. Chapter 7: Widely linear adaptive filters for hypercomplex signals - p. 169 15. Chapter 8: Hypercomplex widely linear processing - p. 205 16. Chapter 9: Computational lidar from space - p. 245 17. Chapter 10: Space-time radio signal processing by photonic beamspace computing - p. 281 18. Chapter 11: Higher-order graph signal processing - p. 321 19. Chapter 12: Topology identification and inference over graphs - p. 351 20. Chapter 13: MIMO communications and signal processing - p. 381 21. Index - p. 419
Multidimensional Signal Processing, Volume 54 in the Handbook of Statistics series is dedicated to presenting the latest developments and methodologies in multidimensional signal processing. The book aims to provide a comprehensive overview of the theories, models, and methods that form the foundation of this field. Chapters in this new release include Robust Parameter Estimation of Two Dimensional Chirp Model, Computability Theory for Multidimensional Signal Processing, Tensor signal processing, Spectral compressed sensing by structured matrix optimization methods, Space-time imaging, Hypercomplex Widely Linear Processing, and much more. The book's chapters are meticulously curated to offer detailed, educational content rather than conventional journal-style articles.
Other chapters cover Hypercomplex phase retrieval, Hypercomplex widely linear estimation, MIMO radar signal processing, Computational lidar, Signal processing applications of higher-dimensional graphs, Space-Time Radio Signal Processing by Photonic Upconversion, Computational imaging, and Topology identification and learning over graphs using multi-dimensional data.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1: Robust parameter estimation of two dimensional chirp model - p. 1 9. Chapter 2: Computability theory for multidimensional signal processing - p. 33 10. Chapter 3: Generalized decompositions for tensor data - p. 67 11. Chapter 4: Spectral compressed sensing via structured matrix optimization - p. 91 12. Chapter 5: Space-time imaging - p. 119 13. Chapter 6: Hypercomplex phase retrieval - p. 147 14. Chapter 7: Widely linear adaptive filters for hypercomplex signals - p. 169 15. Chapter 8: Hypercomplex widely linear processing - p. 205 16. Chapter 9: Computational lidar from space - p. 245 17. Chapter 10: Space-time radio signal processing by photonic beamspace computing - p. 281 18. Chapter 11: Higher-order graph signal processing - p. 321 19. Chapter 12: Topology identification and inference over graphs - p. 351 20. Chapter 13: MIMO communications and signal processing - p. 381 21. Index - p. 419
