What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Artificial intelligence
Sergiy A. Vorobyov mainly focuses on Robustness, Algorithm, MIMO, Adaptive beamformer and Mathematical optimization.
His research on Robustness often connects related areas such as Channel state information.
His Algorithm research is multidisciplinary, relying on both Statistics, Identifiability, Signal subspace and Artificial intelligence.
Sergiy A. Vorobyov interconnects Radar, Signal-to-noise ratio and Control theory in the investigation of issues within MIMO.
The Radar study combines topics in areas such as Waveform and Electronic engineering.
His Adaptive beamformer study combines topics in areas such as Quadratic programming and Minimum-variance unbiased estimator.
His most cited work include:
- Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem (1135 citations)
- Phased-MIMO Radar: A Tradeoff Between Phased-Array and MIMO Radars (267 citations)
- Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming (199 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, MIMO, Mathematical optimization, Beamforming and Communication channel.
His Algorithm research incorporates themes from Subspace topology, Statistics and Signal.
His work carried out in the field of MIMO brings together such families of science as Radar, Upper and lower bounds, Continuous-wave radar and Control theory.
His research integrates issues of Mimo radar, Waveform, Electronic engineering and Signal-to-noise ratio in his study of Radar.
His Mathematical optimization course of study focuses on Relay and Wireless and Resource allocation.
His Beamforming research incorporates elements of Space-time adaptive processing, Wireless sensor network, Sensor array and Robustness.
He most often published in these fields:
- Algorithm (38.75%)
- MIMO (26.57%)
- Mathematical optimization (22.51%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (38.75%)
- Radar (14.76%)
- MIMO (26.57%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Radar, MIMO, Adaptive beamformer and Artificial intelligence.
The concepts of his Algorithm study are interwoven with issues in Vandermonde matrix, Graph and White noise.
He combines subjects such as Waveform and Signal processing with his study of Radar.
His study with MIMO involves better knowledge in Communication channel.
His Adaptive beamformer study incorporates themes from Signal-to-noise ratio and Maximization.
His studies deal with areas such as Smoothing and Pattern recognition as well as Artificial intelligence.
Between 2018 and 2021, his most popular works were:
- Toward Millimeter-Wave Joint Radar Communications: A signal processing perspective (82 citations)
- Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication–Radar (26 citations)
- New Designs on MVDR Robust Adaptive Beamforming Based on Optimal Steering Vector Estimation (16 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary scientific interests are in Algorithm, Radar, MIMO, Signal-to-noise ratio and Applied mathematics.
As part of the same scientific family, Sergiy A. Vorobyov usually focuses on Algorithm, concentrating on Subspace topology and intersecting with Maximum likelihood and Array processing.
His studies in Radar integrate themes in fields like Extremely high frequency, Waveform and Electronic engineering.
His study in MIMO is interdisciplinary in nature, drawing from both Vandermonde matrix and Signal.
His Applied mathematics research integrates issues from Adaptive beamformer, Gradient method, Lipschitz continuity and Relaxation.
The various areas that Sergiy A. Vorobyov examines in his Gradient method study include Line search, Optimization problem, Robustness and Rate of convergence.
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