What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Radar
His primary areas of study are Radar, Algorithm, Clutter, Constant false alarm rate and Electronic engineering.
His study in Radar is interdisciplinary in nature, drawing from both Waveform and Gaussian noise.
His Algorithm research is multidisciplinary, incorporating elements of Detection theory, Statistical power, Radar tracker, Likelihood-ratio test and Signal processing.
His studies deal with areas such as Higher-order statistics, Statistics, Gaussian and Detector as well as Clutter.
The study incorporates disciplines such as False alarm, Stationary target indication, Weibull distribution and Estimator in addition to Constant false alarm rate.
His Electronic engineering study integrates concerns from other disciplines, such as Wireless, Frequency band, White noise and Multistatic radar.
His most cited work include:
- Image fusion techniques for remote sensing applications (319 citations)
- Tracking a ballistic target: comparison of several nonlinear filters (292 citations)
- Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter (232 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Radar, Algorithm, Electronic engineering, Clutter and Artificial intelligence.
His Remote sensing research extends to Radar, which is thematically connected.
His Algorithm study combines topics from a wide range of disciplines, such as Estimator, Gaussian and Detector.
He has included themes like Ambiguity function, Phased array, Signal and Monopulse radar in his Electronic engineering study.
The various areas that Alfonso Farina examines in his Clutter study include Filter, Constant false alarm rate, Interference, Matched filter and Gaussian noise.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition.
He most often published in these fields:
- Radar (43.35%)
- Algorithm (35.22%)
- Electronic engineering (20.44%)
What were the highlights of his more recent work (between 2017-2021)?
- Radar (43.35%)
- Algorithm (35.22%)
- Clutter (19.21%)
In recent papers he was focusing on the following fields of study:
Alfonso Farina mostly deals with Radar, Algorithm, Clutter, Covariance matrix and Interference.
Alfonso Farina studies Radar tracker which is a part of Radar.
His work carried out in the field of Algorithm brings together such families of science as Power, Direction of arrival, Detector, Energy and Multipath propagation.
His Clutter study incorporates themes from Computer hardware, Filter design, Object detection, Doppler effect and Code.
His Covariance matrix research is multidisciplinary, incorporating elements of Covariance, Likelihood-ratio test, Noise and Compressed sensing.
As part of the same scientific family, Alfonso Farina usually focuses on Interference, concentrating on Communication channel and intersecting with Wireless.
Between 2017 and 2021, his most popular works were:
- Waveform design for communicating radar systems using Fractional Fourier Transform (19 citations)
- Focus-before-detection radar signal processing: part ii–recent developments (16 citations)
- New ECCM Techniques Against Noise-like and/or Coherent Interferers (13 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Radar
Radar, Algorithm, Sensor fusion, Covariance matrix and Context are his primary areas of study.
His Radar research integrates issues from Electronic engineering and Human–computer interaction.
The Electronic engineering study combines topics in areas such as Demodulation, Waveform, Bit error rate and Orthogonal frequency-division multiplexing.
His Algorithm research is multidisciplinary, incorporating perspectives in Parametric statistics, Artificial neural network, Finite set, Doppler effect and Radar waveforms.
The Sensor fusion study which covers Law enforcement that intersects with Work, Artificial intelligence, Synthetic aperture radar and Systems engineering.
His Covariance matrix study deals with Likelihood-ratio test intersecting with Statistical classification, Estimation of covariance matrices, Hermitian matrix and Covariance.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
