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Compressive Sensing Techniques for Radar and ESM Applications

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Sensors & Electronics Technology

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Adaptive Signal Processing, Beamforming, Compressive Sensing, Convex Optimization, Data Reduction, ESM, High Resolution Radar, Imaging Radar, Inverse Synthetic Aperture Radar ISAR, Performance Assessment, Robustness, Sparse Signal Representation, STAP, Synthetic Aperture Radar SAR, TWI


Compressive sensing (CS) is a novel acquisition and processing theory that enables reconstruction of sparse signals from a set of non-adaptive measurements sampled at a much lower rate than required by the Nyquist-Shannon sampling theorem as pertaining to the full signal bandwidth. In particular, compressive sensing exploits the fact that the information bandwidth of the signal is much smaller than the full signal bandwidth. The use of CS may lead to several benefits, including but not limited to significant hardware reductions in ESM receivers; unambiguous signal recovery from incomplete measurements (filling in missing data) in interleaved radar modes (SAR/GMTI, multi-function radars) and sparse arrays; high resolution imaging via MIMO radars or multi-pass (e.g. tomography) with significantly less data and/or hardware. Current military operations are facing new challenges, such as spectral congestion, intelligent jamming, and the need for multi-function systems. The traditional approach seems to be insufficient to effectively deal with these issues. CS is a relatively novel technique that provides a new framework under which such issues can be tackled. CS is a promising technology however the integration of this technique into operational systems faces a number of challenges that must be addressed such as computational cost, memory requirements and real time implementation. Another challenge is related to performance assessment and prediction since CS uses unconventional processing schemes. Cooperation amongst several NATO countries coordinated under the umbrella of the task group SET-236 provides a framework for sharing data and algorithms, which are necessary for investigating the performance and robustness of these methods.


The main objective of this Research Lecture Series is to present the cutting edge of CS techniques for Radar and ESM systems and thereby increases the awareness of their value to the NATO scientific and engineering communities. Lectures are given by leading experts in this area and discuss pros & cons of CS. Moreover, the RLS will review current developments in this area. • Assess the performance and robustness of CS based system architectures and algorithms for radar and ESM applications/scenarios; • Identify and quantify benefits and challenges in the transition to operational CS based radar and ESM systems;


The Lecture Series Team will present all relevant aspects of compressive sensing (CS) techniques applied to radar, DOA systems, imaging systems, and ESM systems. The underlying sparse signal reconstruction techniques and their fundamental limits especially with Convex Optimization Techniques in the field of Radar will be demonstrated. The lectures will cover aspects of hardware architectures for compressive sampling and CS signal processing from basic to advanced applications. Tentative lecture topics: • Overview of CS applied to radar (Dr. L. Anitori, TNO, The Netherlands) • Fundamental Limits and Convex Optimization Techniques for estimation problems in Radar (Prof. E. Ertin, Ohio State University, USA) • Hardware architectures for compressive sensing (Prof. E. Ertin, Ohio State University, USA) • CS and Radar Detection (Dr. L. Anitori, TNO, The Netherlands) • CS for Beamforming and Tomography (Dr. M. Weiss, Fraunhofer FHR-PSR, Germany) • CS for Passive and Multistatic Radars (Dr. M. Weiss, Fraunhofer FHR-PSR, Germany) • CS for Inverse SAR (Prof. M. Martorella, University of Pisa, Italy) • Fundamental Limits and Convex Optimization Techniques for estimation problems in Radar (Prof. E. Ertin, Ohio State University, USA) • CS for 2D and 3D SAR (Prof. E. Ertin, Ohio State University, USA) • Applications of CS to GMTI (Dr. Rangaswamy, AFRL, USA)

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