Antonio De Maio is a Professor in the Department of Electrical Engineering and Information Technology at the Università degli Studi di Napoli Federico II, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Yonina C. Eldar is a Professor at the Weizmann Institute of Science. She has authored and edited several books, including Sampling Theory: Beyond Bandlimited Systems (Cambridge, 2015) and Compressed Sensing: Theory and Applications (Cambridge, 2012). She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and Eurasip, and a member of the Israel National Academy of Science and Humanities. Alexander M. Haimovich is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

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• Preface Antonio De Maio, Yonina C. Eldar and Alexander M. Haimovich; 1. Sub-Nyquist radar: principles and prototypes Kumar Vijay Mishra and Yonina C. Eldar; 2. Clutter rejection and adaptive filtering in compressed sensing radar Peter B. Tuuk; 3. RFI mitigation based on compressive sensing methods for UWB radar imaging Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston and Lam H. Nguyen; 4. Compressed CFAR techniques Laura Anitori and Arian Maleki; 5. Sparsity-based methods for CFAR target detection in STAP random arrays Haley H. Kim and Alexander M. Haimovich; 6. Fast and robust sparsity-based STAP method for nonhomogeneous clutter Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long and Tapan K. Sarkar; 7. Super-resolution radar imaging via convex optimization Reinhard Heckel; 8. Adaptive beamforming via sparsity-based reconstruction of covariance matrix Yujie Gu, Nathan A. Goodman and Yimin D. Zhang; 9. Spectrum sensing for cognitive radar via model sparsity exploitation Augusto Aubry, Vincenzo Carotenuto, Antonio De Maio and Mark Govoni; 10. Cooperative spectrum sharing between sparse-sensing-based radar and communication systems Bo Li and Athina P. Petropulu; 11. Compressed sensing methods for radar imaging in the presence of phase errors and moving objects Ahmed Shaharyar Khwaja, Naime Ozben Onhon and Mujdat Cetin.

Antonio De Maio is a Professor in the Department of Electrical Engineering and Information Technology at the Università degli Studi di Napoli Federico II, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Yonina C. Eldar is a Professor at the Weizmann Institute of Science. She has authored and edited several books, including Sampling Theory: Beyond Bandlimited Systems (Cambridge, 2015) and Compressed Sensing: Theory and Applications (Cambridge, 2012). She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and Eurasip, and a member of the Israel National Academy of Science and Humanities. Alexander M. Haimovich is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

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• Preface Antonio De Maio, Yonina C. Eldar and Alexander M. Haimovich; 1. Sub-Nyquist radar: principles and prototypes Kumar Vijay Mishra and Yonina C. Eldar; 2. Clutter rejection and adaptive filtering in compressed sensing radar Peter B. Tuuk; 3. RFI mitigation based on compressive sensing methods for UWB radar imaging Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston and Lam H. Nguyen; 4. Compressed CFAR techniques Laura Anitori and Arian Maleki; 5. Sparsity-based methods for CFAR target detection in STAP random arrays Haley H. Kim and Alexander M. Haimovich; 6. Fast and robust sparsity-based STAP method for nonhomogeneous clutter Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long and Tapan K. Sarkar; 7. Super-resolution radar imaging via convex optimization Reinhard Heckel; 8. Adaptive beamforming via sparsity-based reconstruction of covariance matrix Yujie Gu, Nathan A. Goodman and Yimin D. Zhang; 9. Spectrum sensing for cognitive radar via model sparsity exploitation Augusto Aubry, Vincenzo Carotenuto, Antonio De Maio and Mark Govoni; 10. Cooperative spectrum sharing between sparse-sensing-based radar and communication systems Bo Li and Athina P. Petropulu; 11. Compressed sensing methods for radar imaging in the presence of phase errors and moving objects Ahmed Shaharyar Khwaja, Naime Ozben Onhon and Mujdat Cetin.

Antonio De Maio is a Professor in the Department of Electrical Engineering and Information Technology at the Università degli Studi di Napoli Federico II, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Yonina C. Eldar is a Professor at the Weizmann Institute of Science. She has authored and edited several books, including Sampling Theory: Beyond Bandlimited Systems (Cambridge, 2015) and Compressed Sensing: Theory and Applications (Cambridge, 2012). She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and Eurasip, and a member of the Israel National Academy of Science and Humanities. Alexander M. Haimovich is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

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• Preface Antonio De Maio, Yonina C. Eldar and Alexander M. Haimovich; 1. Sub-Nyquist radar: principles and prototypes Kumar Vijay Mishra and Yonina C. Eldar; 2. Clutter rejection and adaptive filtering in compressed sensing radar Peter B. Tuuk; 3. RFI mitigation based on compressive sensing methods for UWB radar imaging Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston and Lam H. Nguyen; 4. Compressed CFAR techniques Laura Anitori and Arian Maleki; 5. Sparsity-based methods for CFAR target detection in STAP random arrays Haley H. Kim and Alexander M. Haimovich; 6. Fast and robust sparsity-based STAP method for nonhomogeneous clutter Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long and Tapan K. Sarkar; 7. Super-resolution radar imaging via convex optimization Reinhard Heckel; 8. Adaptive beamforming via sparsity-based reconstruction of covariance matrix Yujie Gu, Nathan A. Goodman and Yimin D. Zhang; 9. Spectrum sensing for cognitive radar via model sparsity exploitation Augusto Aubry, Vincenzo Carotenuto, Antonio De Maio and Mark Govoni; 10. Cooperative spectrum sharing between sparse-sensing-based radar and communication systems Bo Li and Athina P. Petropulu; 11. Compressed sensing methods for radar imaging in the presence of phase errors and moving objects Ahmed Shaharyar Khwaja, Naime Ozben Onhon and Mujdat Cetin.