Prof. Dr. Alfonso Farina
Consultant, Rome, Italy
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Academic Career Length
17Years (2010-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications30 July 2019 in IEEE Transactions on Aerospace and Electronic Systems
Journal Article
LYLinjie Yan
Linjie Yan
Pia ADDABBOCHChengpeng Hao
Chengpeng Hao
Danilo OrlandoAlfonso FarinaMultiple-stage adaptive architectures are conceived to face with the problem of target detection buried in noise, clutter, and intentional interference. First, a scenario where the radar system is under the electronic attack of noise-like interferers is considered. In this context, two sets of training samples are jointly exploited to devise a novel two-step estimation procedure of the interference covariance matrix. Then, this estimate is plugged in the adaptive matched filter to mitigate the deleterious effects of the noise-like jammers on radar sensitivity. Besides, a second scenario, which extends the former by including the presence of coherent jammers, is addressed. Specifically, the sparse nature of data is brought to light and the compressive sensing paradigm is applied to estimate target response and coherent jammers amplitudes. The likelihood ratio test, where the unknown parameters are replaced by previous estimates, is designed and assessed. Remarkably, the sparse approach allows for echo classification and estimation of both angles of arrival and number of the interfering sources. The performance analysis, conducted resorting to simulated data, highlights the effectiveness of the newly proposed architectures also in comparison with suitable competing architectures (when they exist).
1 September 2021 in IEEE Aerospace and Electronic Systems Magazine
Journal Article
GSGiovanni Soldi
Giovanni SoldiDGDomenico Gaglione
Domenico GaglioneNFNicola Forti
Nicola Forti
Alessio Di SimoneFDFilippo Cristian Daffina
Filippo Cristian Daffina
Alfonso Farina +8
Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing, and data fusion techniques, is provided in a companion paper, titled: "Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques".
26 February 2021 in IEEE Transactions on Signal Processing
Journal Article
MSMohammad Sadeghi
Mohammad SadeghiFBFreidoon Behnia
Freidoon BehniaRARouhollah Amiri
Rouhollah AmiriAlfonso FarinaIn this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information matrix (FIM), which is equivalent to minimizing the error ellipse area. The square root of the average determinant of the FIM can be expressed as a product of three parameters, namely the equivalent single radar gain, coherency gain and geometry gain. It is shown that the coherency gain of coherent MIMO radar is greater than the non-coherent one, while the geometry gain of coherent MIMO radar is always smaller than or equal to the non-coherent case. The maximum value of the geometry gain for a MIMO radar system with N transmit and M receive antennas is equal to MN for the coherent case while it is 2MN for the non-coherent case.
21 July 2020 in IEEE Transactions on Signal Processing
Journal Article
JLJun Liu
Jun LiuDMDavide Massaro
Davide MassaroDanilo OrlandoAlfonso FarinaIn this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.
1 September 2021 in IEEE Aerospace and Electronic Systems Magazine
Journal Article
GSGiovanni Soldi
Giovanni SoldiDGDomenico Gaglione
Domenico GaglioneNFNicola Forti
Nicola FortiLMLeonardo M. Millefiori
Leonardo M. MillefioriPBPaolo Braca
Paolo BracaAlfonso Farina +8
Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since ground-based radars and automatic identification system (AIS) do not always provide a comprehensive and seamless coverage of the entire maritime domain, the use of space-based sensors is crucial to complement them. We reviewed space-based technologies for MS in the first part of this work, titled “Space-based Global Maritime Surveillance. Part I: Satellite Technologies.” However, MS systems combining multiple terrestrial and space-based sensors with additional information sources require dedicated artificial intelligence and data fusion techniques for processing raw satellite images and fusing heterogeneous information. The second part of our work focuses on some recent promising artificial intelligence and data fusion techniques for MS using space-based sensors.
13 April 2020 in IEEE Transactions on Signal Processing
Journal Article
ANAli Noroozi
Ali NorooziRARouhollah Amiri
Rouhollah AmiriMNMohammad Mahdi Nayebi
Mohammad Mahdi NayebiAlfonso FarinaThis paper deals with the moving target localization problem from time delay and Doppler shift measurements in a distributed multiple-input multiple-output radar system. An algebraic closed-form two-stage weighted least squares solution is presented to locate the target position and velocity. In the first stage, a set of pseudo-linear equations is established by introducing and decreasing the nuisance parameters. Then, two quadratic equations are obtained in terms of the nuisance parameters by considering relationships among them and the target position and velocity. By applying the elimination method that gives the nuisance parameters and substituting them into the localization problem, the target position and velocity are determined in the first stage. In the second stage, the error in the initial solution is estimated to improve the localization performance. The proposed estimator is shown to achieve the Cramer-Rao lower bound performance under Gaussian noise conditions, when the measurement error is small. The great advantage of the proposed method is that it can give the solution with fewer sensors (transmitters or receivers) in comparison with the state-of-the-art algorithms. Simulation results show that when there are one transmitter and three receivers or two transmitters and two receivers, the method can find the solution with a good accuracy whereas the state-of-the-art ones cannot determine the target position and velocity.
About Me
Alfonso Farina obtained the laurea degree in electronic engineering in 1973 at University La Sapienza in Rome. In 1974, he joined Selenia (later renamed Selex ES), a Finmeccanica company. There, he was director of the analysis of integrate systems unit and then chief engineer of large business systems division. More recently, he was senior VP CTO and then senior advisor to the CTO. From 1979 to 1985, he was a professor of radar techniques at the University of Naples. He retired in 2014 but, currently, works as a consultant. His pioneering work on tracking while scanning, now widely used in all radars, was recounted in a classical set of two books that, due to their widespread relevance, were translated also in Russian and Chinese. He is considered the “father” of Italian industrial PCL radar. From 2004 to 2014, he led the team of engineers in conceiving, designing and implementing successive generations of improved PCL radar systems. He is IEEE Fellow (2000), International Fellow of the Royal Academy of Engineering (2005), IET Fellow (1997), EURASIP Fellow (2010), member of European Academy of Science (2020). He won several awards, among which: the Fred Nathanson Memorial Radar Award (1987), the honor of Maestro del Lavoro with decoration of “Stella al Merito del Lavoro” (2003); the IEEE D. J. Picard Medal for Radar Technologies and Applications (2010); the IET Achievement Medals (2014); the IEEE Signal Processing Society Industrial Leader Award (2018).