This book gives you a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies under practical constraints, helping you to meet the more and more stressing sensing system requirements. These include power method-like iterations, coordinate descent, and majorization-minimization, that are utilized for designing radar waveforms under practical constraints.
The book walks you through how radar waveform synthesis is obtained as the solution to a constrained optimization problem such as finite energy, unimodularity (or being constant-modulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by the practical limitations of the real systems. Several approaches in each of these broad frameworks are detailed and various applications of these optimization techniques are described. Focusing on a holistic approach rather than a problem-specific approach, the book shows you what you need to effectively formulate waveform design and understand the flexibility of the framework for adapting to your own specific needs. You’ll have full access to the tools and knowledge you need to design waveform with optimized correlation/cross-correlation properties for SISO/SIMO and MIMO radars, taking into account spectral constraints for cognitive rads, as well as coexistence with communications and mitigate possible Doppler and quantization errors, and more. The book also includes representative software codes that further help you generate the described solutions.
With its unique style of covering mathematical results along with their applications from diverse areas, this is a much-needed, detailed handbook for industry researchers, scientists and designers including medical, marine, defense, and automotive companies. It is also an excellent resource for advanced courses on radar signal processing.
Table Of Contents
Need for practical signal design, Convex and non-convex optimization, Power method-like iterations Majorization minimization (MM) methods, Coordinate descent (CD) and Block Successive Upper-Bound Minimization (BSUM) methods, Other optimization methods, Deep learning for radar, High Resolution and 4D imaging MIMO Radars for Automotive applications , Waveform design in Spectrum sharing applications, Indoor applications, Optimal transmit signal design for Space-Time Adaptive Processing (STAP) in MIMO radar systems, Cognitive radar, prototype, and implementation