Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems (
Advances in digital signal processing algorithms and computer technology have combined to produce real-time systems with capabilities far beyond those of just few years ago. Nonlinear, adaptive methods for signal processing have emerged to provide better array gain performance, however, they lack the robustness of conventional algorithms. The challenge remains to develop a concept that exploits the advantages of both-a scheme that integrates these methods in practical, real-time systems.
The Advanced Signal Processing Handbook helps you meet that challenge. Beyond offering an outstanding introduction to the principles and applications of advanced signal processing, it develops a generic processing structure that takes advantage of the similarities that exist among radar, sonar, and medical imaging systems and integrates conventional and nonlinear processing schemes.
Why Read This Book
You should read this book if you need a practical bridge between advanced signal‑processing theory and real‑time system implementation across radar, sonar, and medical imaging. It shows how to combine adaptive and conventional techniques, and how to architect algorithms for real‑time DSP hardware and software environments.
Who Will Benefit
Engineers and researchers working on radar/sonar/medical imaging systems who need to move advanced adaptive and nonlinear DSP algorithms into robust, real‑time implementations.
Level: Advanced — Prerequisites: Solid grounding in linear systems, probability and stochastic processes, digital signal processing (filters, FFTs), and basic linear algebra; familiarity with MATLAB and C/C++ is helpful.
Key Takeaways
- Design and analyze adaptive array processors and beamformers for interferer suppression and improved array gain.
- Apply statistical detection and estimation methods to practical radar/sonar and imaging problems.
- Integrate nonlinear and adaptive algorithms with conventional linear methods for improved robustness and performance.
- Architect and optimize signal processing chains for real‑time execution on DSPs, FPGAs, and embedded platforms.
- Implement imaging and reconstruction techniques (beamforming, Doppler processing, tomographic methods) for medical and remote‑sensing systems.
Topics Covered
- Introduction: Trends in Advanced Signal Processing and Real‑Time Systems
- Signal and Array Models for Radar, Sonar, and Imaging
- Statistical Detection and Estimation Fundamentals
- Adaptive Filtering and Adaptive Array Processing
- Beamforming and Space‑Time Processing
- Doppler and Spectral Analysis for Moving Targets
- Nonlinear and Robust Signal Processing Methods
- Imaging and Tomographic Reconstruction Techniques
- Performance Analysis and Metrics for Real Systems
- Real‑Time Architectures: DSPs, FPGAs, and Embedded Systems
- Implementation Issues: Algorithms to Code, Latency, and Throughput
- Case Studies: Radar, Sonar, and Medical Imaging Systems
- Future Directions and Practical Guidelines
Languages, Platforms & Tools
How It Compares
Covers similar application‑level ground as Van Trees' detection/estimation and Haykin's adaptive filtering but places more emphasis on practical, cross‑domain real‑time implementation and system architecture.
