Sparse reconstruction and design through randomization have played significant roles in the history of radar signal processing. A recent series of theoretical and algorithmic results known as compressive or compressed sensing (CS) has ignited renewed interest in applying these ideas to radar problems. A flurry of research has explored the application of CS approaches as well as closely related sparse reconstruction (SR) techniques to a wide range of radar problems. This chapter will provide some historical context for CS, describe the existing theoretical results and current research directions, highlight several key algorithms that have emerged from these investigations, and offer a few examples of the application of these ideas to radar.

Chapter Contents:

• 5.1 Introduction
• 5.2 CS Theory
• 5.3 SR Algorithms
• 5.4 Sample Radar Applications
• 5.5 Summary
• 5.6 Further Reading
• 5.7 Acknowledgments
• 5.8 References
• 5.9 Problems

Inspec keywords: signal reconstruction; compressed sensing; radar signal processing

Other keywords: radar problem; radar application; compressed sensing; SR technique; CS; sparse reconstruction; randomization; radar signal processing

Subjects: Radar equipment, systems and applications; Signal processing and detection