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Full Description
Multidimensional Signal Processing volume of 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. Our goal is to cater to a broad audience of signal-
processing practitioners. The chapters within this volume are meticulously curated to offer
detailed, educational content rather than conventional journal-style articles. Contributors to this
volume are leading experts in the field, ensuring high-profile content that combines theoretical
rigor with practical insights. Through this comprehensive compilation, we aim to advance the
knowledge and application of multidimensional signal processing, contributing significantly to
the broader fields of statistics and data science.
Contents
Preface
1. Robust Parameter Estimation of Two Dimensional Chirp Model
Debassis Kundu
2. Computability Theory for Multidimensional Signal Processing
Holger Boche, Volker Pohl and H. VIncent Poor
3. Tensor signal processing
David Hong
4. Spectral compressed sensing by structured matrix optimization methods
Jian-Feng Cai, Xunmeng Wu, Zai Yang and Juntao You
5. Space-time imaging
David Brady
6. Hypercomplex Widely Linear Processing
Sayed Pouria Talebi and Clive Cheong Took
7. Hypercomplex phase retrieval
Kumar Vijay Mishra, Henry Arguello and Brian M. Sadler
8. Hypercomplex widely linear estimation
Wenyuan Wang and Kutluyil Dogancay
9. MIMO radar signal processing
Sergiy A. Vorobyov and Visa Koivunen
10. Computational lidar
Gonzalo R. Arce
11. Signal processing applications of higher-dimensional graphs
Santiago Segarra
12. Space-Time Radio Signal Processing by Photonic Upconversion
Xiao-Feng Qi and Dennis W. Prather
13. Computational imaging
Ayush Bhandari
14. Topology identification and learning over graphs using multi-dimensional data
Gonzalo Mateos, Georgios Giannakis, Yanning Shen and Ananthram Swami
