Compressed Sensing: Applications in Radar and Communications

If information bandwidth less than total bandwidth, then should be able to sample below Nyquist without information loss and recover missing samples by convex optimization. (Emmanuel Candes, “Compressive Sensing—A 25 Minute ` Tour,” EU-US Frontiers of Engineering Symposium, Cambridge, September 2010) Compressed Sensing is an emerging approach exploiting the sparsity feature of a signal to give accurate waveform representation at reduced sampling rate, below the ShannonNyquist conditions, thus leading to efficient radar and communication systems, with reduced complexity and cost. The aim of this special issue is to provide an international forum for experts and researchers working in the area of Compressed Sensing applied to radar and communication contexts, in order to explore the state of the art of such techniques and to present new advanced concepts and results. This special issue collects 6 papers from 14 authors belonging to different countries and institutions. It summarizes the most recent developments and ideas on emerging Compressed Sensing approaches, with particular focus addressed to the following issues: (i) Compressed Sensing for signal processing. (ii) Compressed Sensing for MIMO architectures. (iii) Compressed Sensing for inverse scattering. (iv) Compressed Sensing for high-resolution radars. (v) Compressed Sensing for wireless communications and networks. In the paper by S. Costanzo entitled “Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar,” a Compressed Sensing formulation is adopted to enhance the range resolution of narrow-band radars. In the paper by M. Minner entitled “Compressed Sensing in On-Grid MIMO Radar,” the feasibility of Compressed Sensing-based MIMO radar to detect on-grid targets in the azimuth, time-delay, and Doppler domain is investigated. The paper by M. E. Dom´ınguez-Jimenez et al. entitled ´ “Estimation of Symmetric Channels for Discrete Cosine Transform Type-I Multicarrier Systems: A Compressed Sensing Approach” explores the application of Compressive Sensing to the problem of channel estimation for multicarrier communications. In the paper by S. K. Bolisetti et al. entitled “Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter,” the target detection performances of MIMO radars in the presence of clutter are optimized by adopting a compressive GLRT detector.