Advanced Techniques of Radar Detection in Non-Gaussian Background


The Long Island (LI) Chapter of IEEE Signal Processing Society (SPS) and Aerospace and Electronic Systems (AES) in collaboration with the Educational Activities Committee (EAC) of IEEE LI and Renewable Energy and Sustainability Center (RESC) of Farmingdale State College invites you to the presentation:

Advanced Techniques of Radar Detection in Non-Gaussian Background
Dr. Maria Sabrina Greco, Associate Professor at University of Pisa, IEEE Distinguished Lecturer

For several decades, the Gaussian assumption on the disturbance modeling in radar systems has been widely used to deal with detection problems. But, in modern high-resolution radar systems, the disturbance cannot be modeled as Gaussian distributed and the classical detectors suffer from high losses. In this talk, after a brief description of modern statistical and spectral models for high-resolution clutter, coherent optimum, and sub-optimum detectors, designed for such a background, will be presented and their performance analyzed against a non-Gaussian disturbance. Different interpretations of the various detectors are provided that highlight the relationships and the differences among them. After this first part, some discussion will be dedicated to how to make adaptive the detectors, by incorporating a proper estimate of the disturbance covariance matrix. Recent works on Maximum Likelihood and robust covariance matrix estimation have proposed different approaches such as the Approximate ML (or Fixed-Point) Estimator or the M-estimators. These techniques allow improving the detection performance regarding false alarm regulation and detection gain in SNR. Some of the results with simulated and real recorded data will be shown.

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