Geert Leus
Delft University of Technology, Delft, The Netherlands
Compressive Covariance Sensing for Radar Applications


Location: Sala Conferenze, Polo Piagge
Time: Tuesday, June 16 2015, h. 15.00

Tutorial Motivation and Relevance
There are many engineering applications that rely on frequency or angular spectrum sensing, such as cognitive radio, radio astronomy, seismic acquisition, and radar, where target detection and the estimation of Doppler frequencies and directions-of-arrival (DoAs) are of interest. Many of these applications do not require the reconstruction of the full signal, and can perfectly rely on an estimate of the frequency or angular power spectral density (PSD), or in other words, the second-order statistics of the signal. However, high temporal and spatial sampling rates are generally required leading to high costs, which can be prevented by a direct compression step carried out in the analog domain (e.g., by means of a modulated wideband converter, multi-coset sampling, analog beamforming, antenna selection, etc.). This leads to the problem of sensing the frequency or angular PSD (or the Doppler frequencies and DoAs of the targets) using compressive observations, labeled as compressive covariance sensing (CCS).

In this tutorial, an overview will be given of the state-of-the-art in CCS as well as the related connections to compressive sensing (CS). The design constraints of the compression matrices will be discussed, which are completely different as in classical CS, and the estimation-detection techniques to sense the covariance using compressive measurements will be presented. In this context, both sparse and dense sampling techniques will be discussed. Also distributed CCS is proposed, where compressive measurements in one domain are fused in the dual domain, i.e., temporal compressive measurements are gathered at different spatial antennas or spatial compressive measurements from different time slots are combined. We end this tutorial by sketching some open issues and presenting the concluding remarks.

Tutorial Outline
  • Introduction
    • Frequency and Angular Spectrum Sensing
    • Compressive Spectrum Sensing
    • Radar Applications
  • Compressive Covariance Sampling
    • Basic Principles
    • Covariance Structures
    • Estimation versus Detection
  • Covariance Estimation
    • Least Squares
    • Maximum Likelihood
    • Regularized Estimators
  • Covariance Detection
    • Neyman-Pearson
    • Multiple Hypothesis Testing
  • Sparse Covariance Sampling
    • Linear Sparse Rulers
    • Circular Sparse Rulers
    • Dynamic Non-Uniform Sampling
    • Compression Limits
  • Dense Covariance Sampling
    • Sampler Designs
    • Dynamic Random Sampling
    • Compression Limits
  • Distributed Covariance Sensing
    • Sparse versus Dense Sampling
    • Samples/Sensors Trade-Off
  • Open Issues
  • Conclusions

Geert Leus received the electrical engineering degree and the PhD degree in applied sciences from the Katholieke Universiteit Leuven, Belgium, in June 1996 and May 2000, respectively. Currently, Geert Leus is an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the area of signal processing for communications and networking. Geert Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a Fellow of the IEEE. Geert Leus was the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, and an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is a Member-at-Large to the Board of Governors of the IEEE Signal Processing Society and a member of the IEEE Sensor Array and Multichannel Technical Committee. He finally serves as the Editor in Chief of the EURASIP Journal on Advances in Signal Processing.

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