What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Arye Nehorai spends much of his time researching Algorithm, Estimation theory, Signal processing, Sensor array and Statistics.
The various areas that Arye Nehorai examines in his Algorithm study include Radar, Estimator, Mathematical optimization and Speech recognition.
His work deals with themes such as Electromagnetic vector sensor, Polarization, Covariance, Linear independence and Artificial intelligence, which intersect with Estimation theory.
His study in Signal processing is interdisciplinary in nature, drawing from both Polarimetry, Detection theory and Constant false alarm rate.
His Sensor array research focuses on Array processing and how it connects with Noise, Mean squared error, Parameterized complexity and Finite set.
His research in Statistics tackles topics such as Direction of arrival which are related to areas like Grid and Coprime integers.
His most cited work include:
- Posterior Cramer-Rao bounds for discrete-time nonlinear filtering (981 citations)
- Performance study of conditional and unconditional direction-of-arrival estimation (906 citations)
- Vector-sensor array processing for electromagnetic source localization (518 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Algorithm, Estimation theory, Radar, Mathematical optimization and Artificial intelligence.
His Algorithm study combines topics in areas such as Statistics, Sensor array, Estimator and Signal processing.
Arye Nehorai interconnects Acoustics and Direction of arrival in the investigation of issues within Sensor array.
His primary area of study in Estimation theory is in the field of Cramér–Rao bound.
Arye Nehorai has included themes like Waveform, Electronic engineering and Orthogonal frequency-division multiplexing in his Radar study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Computer vision and Pattern recognition.
He most often published in these fields:
- Algorithm (39.86%)
- Estimation theory (15.64%)
- Radar (14.78%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (39.86%)
- Artificial intelligence (13.23%)
- Pattern recognition (6.19%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Algorithm, Artificial intelligence, Pattern recognition, Radar and Clutter.
His studies deal with areas such as Narrowband, Synthetic aperture radar, MIMO, Sparse matrix and Noise as well as Algorithm.
His research investigates the connection between MIMO and topics such as Sampling that intersect with problems in Estimation theory.
His Image processing study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Workflow, bridging the gap between disciplines.
His Clutter study also includes
- Kernel density estimation that connect with fields like Distance measures,
- Matching pursuit and related Data mining,
- Waveform that intertwine with fields like Covariance matrix,
- Constant false alarm rate, which have a strong connection to Radar engineering details.
Arye Nehorai works mostly in the field of Compressed sensing, limiting it down to topics relating to Sensor array and, in certain cases, Signal processing, as a part of the same area of interest.
Between 2015 and 2021, his most popular works were:
- Coarrays, MUSIC, and the Cramér–Rao Bound (126 citations)
- The Random Frequency Diverse Array: A New Antenna Structure for Uncoupled Direction-Range Indication in Active Sensing (92 citations)
- DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering (81 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of study are Algorithm, Artificial intelligence, Computer vision, Pattern recognition and Control theory.
His Cramér–Rao bound study in the realm of Algorithm connects with subjects such as Uncorrelated.
His research integrates issues of Covariance and Hilbert space in his study of Artificial intelligence.
His biological study spans a wide range of topics, including Gait and Filter.
His Pattern recognition study integrates concerns from other disciplines, such as Basis, Microscopy, Position, Superresolution and Noise reduction.
His Control theory study combines topics from a wide range of disciplines, such as Kinematics and Rigid body.
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