Signal Processing for Multistatic Radar Systems
Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms
- 1st Edition - October 26, 2019
- Latest edition
- Authors: Ngoc Hung Nguyen, Kutluyil Doğançay
- Language: English
Signal Processing for Multistatic Radar Systems: Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms addresses three important aspects of signal… Read more
Signal Processing for Multistatic Radar Systems: Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms addresses three important aspects of signal processing for multistatic radar systems, including adaptive waveform selection, optimal geometries and pseudolinear tracking algorithms. A key theme of the book is performance optimization for multistatic target tracking and localization via waveform adaptation, geometry optimization and tracking algorithm design. Chapters contain detailed mathematical derivations and algorithmic development that are accompanied by simulation examples and associated MATLAB codes. This book is an ideal resource for university researchers and industry engineers in radar, radar signal processing and communications engineering.
- Develops waveform selection algorithms in a multistatic radar setting to optimize target tracking performance
- Assesses the optimality of a given target-sensor geometry and designs optimal geometries for target localization using mobile sensors
- Gives an understanding of low-complexity and high-performance pseudolinear estimation algorithms for target localization and tracking in multistatic radar systems
- Contains the MATLAB codes for the examples used in the book
Researchers and graduate students in signal processing, radar and communications engineering
1. Introduction
Part 1. Adaptive waveform selection
2. Waveform selection for multistatic tracking of a maneuvering Target
3. Waveform selection for multistatic target tracking in clutter
4. Waveform selection for multistatic target tracking with Cartesian estimates
5. Waveform selection for distributedmultistatic target tracking
Part 2. Optimal geometry analysis
6. Optimal geometries for multistatic target localization with one transmitter and multiple receivers
7. Optimal geometries for multistatic target localization by independent bistatic channels
Part 3. Pseudolinear tracking algorithms
8. Batch track estimators formultistatic target motion analysis
9. Closed-form solutions for multistatic target localization with time-difference-of-arrival measurements
Part 1. Adaptive waveform selection
2. Waveform selection for multistatic tracking of a maneuvering Target
3. Waveform selection for multistatic target tracking in clutter
4. Waveform selection for multistatic target tracking with Cartesian estimates
5. Waveform selection for distributedmultistatic target tracking
Part 2. Optimal geometry analysis
6. Optimal geometries for multistatic target localization with one transmitter and multiple receivers
7. Optimal geometries for multistatic target localization by independent bistatic channels
Part 3. Pseudolinear tracking algorithms
8. Batch track estimators formultistatic target motion analysis
9. Closed-form solutions for multistatic target localization with time-difference-of-arrival measurements
- Edition: 1
- Latest edition
- Published: October 26, 2019
- Language: English
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Ngoc Hung Nguyen
Ngoc Hung Nguyen received the B.E. degree (Hons.) in electrical and electronic engineering from the University of Adelaide, Australia, in 2012, and the Ph.D. degree in telecommunications from the University of South Australia, Australia, in 2016. He is currently a Researcher with the Defence Science and Technology Group, Australia, and an Adjunct Research Fellow with the University of South Australia. Prior to this, between February 2016 and February 2019 he was a Research Fellow and Lecturer with the University of South Australia. His research interests include statistical and adaptive signal processing, compressive sensing, and estimation theory with emphasis on target localization and tracking, sensor management, and radar imaging. Dr. Nguyen currently serves on the Editorial Board of Digital Signal Processing.
Affiliations and expertise
Research Fellow, School of Engineering, University of South Australia, Adelaide, AustraliaKD
Kutluyil Doğançay
Kutluyıl Doğançay received the BS degree with honors in electrical and electronic engineering from Boğaziçi University, Istanbul, Turkey, in 1989, the MSc degree in communications and signal processing from Imperial College, The University of London, UK, in 1992, and the PhD degree in telecommunications engineering from The Australian National University, Canberra, ACT, Australia, in 1996. Since November 1999, he has been with the School of Engineering, University of South Australia, where he is a professor and discipline leader of electrical and mechatronic engineering. His research interests span statistical and adaptive signal processing with applications in defence and communication systems. Dr Doğançay received the Best Researcher Award of School of Engineering, University of South Australia, in 2015, and Tall Poppy Science Award of the Australian Institute of Political Science in 2005. He was the Tutorials Chair of the IEEE Statistical Signal Processing Workshop (SSP 2014), and the Signal Processing and Communications Program Chair of the 2007 Information, Decision and Control Conference. He serves on the Editorial Board of Signal Processing and the EURASIP Journal on Advances in Signal Processing. From 2009-2015 he was an elected member of the Signal Processing Theory and Methods (SPTM) Technical Committee of the IEEE Signal Processing Society. He is currently an associate member of the Sensor Array and Multichannel (SAM) Technical Committee and a member of the IEEE ComSoc Signal Processing for Communications and Electronics Technical Committee. Dr Doğançay is the EURASIP liaison for Australia.
Affiliations and expertise
Professor, School of Engineering, University of South Australia, Adelaide, AustraliaRead Signal Processing for Multistatic Radar Systems on ScienceDirect