What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Yaakov Bar-Shalom mainly investigates Algorithm, Artificial intelligence, Tracking, Control theory and Sensor fusion.
His work focuses on many connections between Algorithm and other disciplines, such as Detection theory, that overlap with his field of interest in Machine learning.
The various areas that Yaakov Bar-Shalom examines in his Artificial intelligence study include Clutter, Computer vision and Pattern recognition.
His Tracking research also works with subjects such as
- Radar that intertwine with fields like Benchmark and Electronic engineering,
- Estimation that connect with fields like Linear system.
His Control theory study combines topics from a wide range of disciplines, such as Estimator, Filter, Covariance and Moving target indication.
Yaakov Bar-Shalom interconnects Assignment problem and Computational complexity theory in the investigation of issues within Sensor fusion.
His most cited work include:
- Estimation with Applications to Tracking and Navigation (3415 citations)
- Tracking and data association (3364 citations)
- The interacting multiple model algorithm for systems with Markovian switching coefficients (1914 citations)
What are the main themes of his work throughout his whole career to date?
Yaakov Bar-Shalom mainly focuses on Algorithm, Control theory, Artificial intelligence, Tracking and Estimator.
His Algorithm research incorporates elements of Radar, Upper and lower bounds, Covariance and Sensor fusion.
His study on Control theory is mostly dedicated to connecting different topics, such as Estimation theory.
The Artificial intelligence study combines topics in areas such as Clutter, Computer vision and Pattern recognition.
Yaakov Bar-Shalom is interested in Tracking system, which is a field of Tracking.
His Estimator research includes elements of Likelihood function, Observability and Mathematical optimization.
He most often published in these fields:
- Algorithm (36.47%)
- Control theory (28.10%)
- Artificial intelligence (26.91%)
What were the highlights of his more recent work (between 2012-2021)?
- Algorithm (36.47%)
- Tracking (26.31%)
- Control theory (28.10%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Algorithm, Tracking, Control theory, Artificial intelligence and Cramér–Rao bound.
The concepts of his Algorithm study are interwoven with issues in Covariance, Estimator, Mathematical optimization, Radar and Monte Carlo method.
In general Tracking study, his work on Tracking system often relates to the realm of Field, thereby connecting several areas of interest.
His Observability and Kalman filter study in the realm of Control theory interacts with subjects such as Fuel gauge.
The study incorporates disciplines such as Clutter, Computer vision and Pattern recognition in addition to Artificial intelligence.
His research in Cramér–Rao bound intersects with topics in Point spread function, Least squares and Noise.
Between 2012 and 2021, his most popular works were:
- Open circuit voltage characterization of lithium-ion batteries (105 citations)
- Battery state of charge tracking, equivalent circuit selection and benchmarking (68 citations)
- Decorrelated unbiased converted measurement Kalman filter (57 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His main research concerns Control theory, Tracking, Artificial intelligence, Algorithm and Battery.
He is interested in Kalman filter, which is a branch of Control theory.
Yaakov Bar-Shalom has researched Tracking in several fields, including Estimator, Real-time computing, Filter and Noise.
Yaakov Bar-Shalom combines subjects such as Trajectory, Propagation delay and Sensor fusion with his study of Filter.
His work deals with themes such as Clutter, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His Algorithm research is multidisciplinary, incorporating perspectives in Mathematical optimization and Radar tracker.
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