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Maria Greco
University of Pisa
272Publications
28H-index
3,180Citations
Publications 272
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#1Maria Greco (UniPi: University of Pisa)H-Index: 28
#2Remy Boyer (CentraleSupélec)H-Index: 16
Abstract The Angular Resolution Limit (ARL) is a fundamental statistical metric to quantify our ability to resolve two closely-spaced narrowband far-field complex sources. This statistical quantity, is defined as the minimal angular deviation between the two sources to be separated for a prefixed detection-based performance. In this work, we assume that the sources of interest are corrupted by a compound-Gaussian noise. In the standard literature, denoting with δ the true distance between the tw...
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#1Stefano Fortunati (UniPi: University of Pisa)H-Index: 10
#2Fulvio Gini (UniPi: University of Pisa)H-Index: 34
Last.Muralidhar Rangaswamy (AFRL: Air Force Research Laboratory)H-Index: 27
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The main aim of this paper is to extend the semiparametric inference methodology, recently investigated for Real Elliptically Symmetric (RES) distributions, to Complex Elliptically Symmetric (CES) distributions. The generalization to the complex field is of fundamental importance in all practical applications that exploit the complex representation of the acquired data. Moreover, the CES distributions has been widely recognized as a valuable and general model to statistically describe the non-Ga...
3 CitationsSource
#1Luke RosenbergH-Index: 13
#2Simon Watts (UCL: University College London)H-Index: 18
Last.Maria Greco (UniPi: University of Pisa)H-Index: 28
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One of the key requirements of a radar system is to detect targets against the background interference. This can be a challenging problem in the maritime domain when the sea moves in a complex manner and the characteristics are time-varying or nonstationary. Understanding the characteristics of sea clutter is therefore essential in developing effective and robust detection schemes. The first part of this tutorial provides details of the relevant statistical models from the literature which are t...
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#1Victor Solo (UNSW: University of New South Wales)H-Index: 18
#2Maria Greco (UniPi: University of Pisa)H-Index: 28
Last.Monica F. Bugallo (SBU: Stony Brook University)H-Index: 20
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The anniversary of a number of significant signal processing algorithms from the 1960s, including the least mean square algorithm and the Kalman filter, provided an opportunity at ICASSP 2019 to reflect on the links between education and innovation. This led ultimately to the proposal of some special sessions as well a panel session that would provide some insight, via a historical perspective, consideration of the current status, and an assessment of the emerging educational future.
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#1Maria Greco (UniPi: University of Pisa)H-Index: 28
#2Jian Li (UF: University of Florida)H-Index: 75
Last.Abdelhak M. ZoubirH-Index: 32
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The articles in this special section focus on recent advancements in radar systems for modern civilian and commercial applications.
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#1Sergio Saponara (UniPi: University of Pisa)H-Index: 26
#2Maria Greco (UniPi: University of Pisa)H-Index: 28
Last.Fulvio Gini (UniPi: University of Pisa)H-Index: 34
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This article addresses the signal processing challenges for the design of a radar-on-chip/in-package in the autonomous-driving era, taking into account recent integration trends and technology capabilities. Radar signal processing platform specifications are discussed, and the radar sensor is compared with other competing sensors, such as lidars, ultrasonics, and video cameras, that aim at detecting still or moving objects and measuring their motion parameters. This survey first focuses on signa...
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#1Maria Greco (UniPi: University of Pisa)H-Index: 28
#2Jian Li (UF: University of Florida)H-Index: 75
Last.Abdelhak M. Zoubir (Technische Universität Darmstadt)H-Index: 32
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The articles in this special section focus on advancements in radar systems for current commercial applications. Radar was developed during World War II for defense and security applications, and it was initially used for detecting aircrafts and missiles, replacing short range and narrow field of- view acoustic devices. This special section is part of a two-part series. The goal of both parts is to show the main techniques applied in different scenarios by different systems, focusing particularl...
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#1Raffaele GrassoH-Index: 12
#2Paolo BracaH-Index: 21
Last.Maria GrecoH-Index: 28
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Stefano Fortunati (UniPi: University of Pisa)H-Index: 10
#2Luca Sanguinetti (UniPi: University of Pisa)H-Index: 23
Last.Fulvio Gini (UniPi: University of Pisa)H-Index: 2
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This work focuses on target detection in a colocated MIMO radar system. Instead of exploiting the "classical’ temporal domain, we propose to explore the spatial dimension (i.e., number of antennas M) to derive asymptotic results for the detector. Specifically, we assume no a priori knowledge of the statistics of the autoregressive data generating process and propose to use a mispecified Wald-type detector, which is shown to have an asymptotic χ-squared distribution as M → ∞. Closed-form expressi...
2 CitationsSource
#1Wei Xiong (NUAA: Nanjing University of Aeronautics and Astronautics)H-Index: 1
#2Maria Greco (UniPi: University of Pisa)H-Index: 28
Last.Xiaobo Deng (Aviation Industry Corporation of China)
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Abstract Focusing on the signal to interference-plus-noise ratio (SINR) maximization in colocated multiple-input multiple-output (MIMO) radars, using the transmit covariance matrix (TCM) design of transmit waveforms, we have proposed a TCM Rpm with the form of symmetrical Toeplitz matrix, where m is the control parameter to generate different TCM. The main features include (a) full rank, to exploit the waveform diversity advantage of MIMO radar and to further suppress the maximum number of inter...
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