What is he best known for?
The fields of study he is best known for:
- Control theory
- Mathematical analysis
- Statistics
John B. Moore mainly focuses on Control theory, Linear system, Algorithm, Mathematical optimization and Optimal control.
As part of his studies on Control theory, John B. Moore often connects relevant areas like Control engineering.
His studies in Linear system integrate themes in fields like Lyapunov function, Minimization problem, Stability theory, Linear-quadratic-Gaussian control and Kalman filter.
The Kalman filter study combines topics in areas such as Filtering theory, Sensor fusion and Signal processing.
His biological study spans a wide range of topics, including Smoothing, Strong consistency, Toeplitz matrix and Markov model.
His study on Optimization problem is often connected to GRASP as part of broader study in Mathematical optimization.
His most cited work include:
- Optimal Filtering (3289 citations)
- Optimal Control: Linear Quadratic Methods (2458 citations)
- Linear Optimal Control (1188 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, Mathematical optimization, Algorithm, Applied mathematics and Linear system are his primary areas of study.
His study in Adaptive control, Control theory, Linear-quadratic-Gaussian control, Nonlinear system and Optimal control is done as part of Control theory.
John B. Moore focuses mostly in the field of Linear-quadratic-Gaussian control, narrowing it down to matters related to Linear-quadratic regulator and, in some cases, Riccati equation and Algebraic Riccati equation.
John B. Moore has included themes like Estimation theory, State, Quadratic equation, System identification and Rate of convergence in his Mathematical optimization study.
His studies deal with areas such as Kalman filter, White noise, Hidden Markov model and Signal processing as well as Algorithm.
His research brings together the fields of Control system and Linear system.
He most often published in these fields:
- Control theory (43.05%)
- Mathematical optimization (29.68%)
- Algorithm (20.59%)
What were the highlights of his more recent work (between 1997-2019)?
- Mathematical optimization (29.68%)
- Algorithm (20.59%)
- Applied mathematics (19.52%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Mathematical optimization, Algorithm, Applied mathematics, Control theory and Hidden Markov model.
His Mathematical optimization research is multidisciplinary, incorporating elements of Computational complexity theory, Estimation theory, Ordinary differential equation and Rate of convergence.
John B. Moore works mostly in the field of Algorithm, limiting it down to concerns involving Signal processing and, occasionally, Noise.
John B. Moore interconnects Discrete mathematics and Linear system, Mathematical analysis in the investigation of issues within Applied mathematics.
In his study, Time of arrival is inextricably linked to Pulse wave, which falls within the broad field of Control theory.
While the research belongs to areas of Hidden Markov model, he spends his time largely on the problem of Markov chain, intersecting his research to questions surrounding Markov process and State space.
Between 1997 and 2019, his most popular works were:
- Direct Kalman filtering approach for GPS/INS integration (210 citations)
- A Newton-like method for solving rank constrained linear matrix inequalities (149 citations)
- Indefinite Stochastic Linear Quadratic Control and Generalized Differential Riccati Equation (122 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Statistics
- Control theory
His primary scientific interests are in Mathematical optimization, Algorithm, Control theory, Linear-quadratic regulator and Rate of convergence.
He combines subjects such as Adaptive filter, Newton's method and Markov chain, Markov model with his study of Mathematical optimization.
In general Algorithm, his work in Linear programming, Estimation theory and Dykstra's projection algorithm is often linked to GRASP linking many areas of study.
His study in Control theory is interdisciplinary in nature, drawing from both Pulse and Signal processing.
Linear system is closely connected to Linear-quadratic-Gaussian control in his research, which is encompassed under the umbrella topic of Linear-quadratic regulator.
His Linear system study integrates concerns from other disciplines, such as Quadratic programming, Optimal control and Differential equation.
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