What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Artificial intelligence
Algorithm, Estimation theory, Signal processing, Mathematical optimization and Covariance matrix are his primary areas of study.
Petre Stoica has included themes like MIMO, Estimator, Control theory and Noise in his Algorithm study.
Petre Stoica interconnects Covariance, Direction of arrival and Artificial intelligence in the investigation of issues within Estimation theory.
His research in Signal processing intersects with topics in Radar, Subspace topology, Waveform and Minification.
As part of the same scientific family, Petre Stoica usually focuses on Mathematical optimization, concentrating on Applied mathematics and intersecting with Simple, Identification and Nonlinear system.
His study on Covariance matrix also encompasses disciplines like
- Array processing which intersects with area such as Sensor array and Pattern recognition,
- Matrix which is related to area like Fisher information.
His most cited work include:
- System identification (5213 citations)
- MUSIC, maximum likelihood, and Cramer-Rao bound (2108 citations)
- Introduction to spectral analysis (1844 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Algorithm, Signal processing, Estimation theory, Mathematical optimization and Estimator.
His research on Algorithm focuses in particular on Covariance matrix.
His work carried out in the field of Signal processing brings together such families of science as Waveform, Electronic engineering, Noise and Pattern recognition.
His study in Estimation theory is interdisciplinary in nature, drawing from both Autoregressive model, Control theory, Applied mathematics and System identification.
The study incorporates disciplines such as Computational complexity theory, Subspace topology, Likelihood function and Maximum likelihood sequence estimation in addition to Mathematical optimization.
The Estimator study combines topics in areas such as Amplitude, Capon and Noise.
He most often published in these fields:
- Algorithm (46.39%)
- Signal processing (30.54%)
- Estimation theory (26.36%)
What were the highlights of his more recent work (between 2010-2021)?
- Signal processing (30.54%)
- Algorithm (46.39%)
- Electronic engineering (13.05%)
In recent papers he was focusing on the following fields of study:
Petre Stoica mostly deals with Signal processing, Algorithm, Electronic engineering, Waveform and Radar.
His work deals with themes such as MIMO, Mathematical optimization and Artificial intelligence, Sonar, which intersect with Signal processing.
Petre Stoica is interested in Estimation theory, which is a field of Algorithm.
His Estimation theory research incorporates themes from Spice, Range and Applied mathematics.
His work focuses on many connections between Electronic engineering and other disciplines, such as Control theory, that overlap with his field of interest in Root-raised-cosine filter and Interference.
His Radar research is multidisciplinary, incorporating perspectives in Covariance matrix and Compressed sensing.
Between 2010 and 2021, his most popular works were:
- SPICE: A Sparse Covariance-Based Estimation Method for Array Processing (333 citations)
- Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging (188 citations)
- New Method of Sparse Parameter Estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data (160 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Artificial intelligence
Petre Stoica focuses on Signal processing, Algorithm, Estimation theory, Radar and Artificial intelligence.
His studies deal with areas such as Control theory, MIMO, Sparse approximation, Sonar and Electronic engineering as well as Signal processing.
His biological study spans a wide range of topics, including Spectral density estimation, Statistics, Estimator and Mathematical optimization.
His research integrates issues of Spice, Signal-to-noise ratio, Covariance matrix, Noise and System identification in his study of Estimation theory.
His Covariance matrix research includes elements of Signal and Beamforming.
His Artificial intelligence research incorporates elements of Computer vision and Pattern recognition.
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