What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of study are Algorithm, Particle filter, Artificial intelligence, Auxiliary particle filter and Mathematical optimization.
His work carried out in the field of Algorithm brings together such families of science as Probability distribution, Sampling, Monte Carlo method, Importance sampling and Bayesian probability.
He has researched Monte Carlo method in several fields, including Estimation theory, Speech recognition and Estimator.
His research integrates issues of Wireless sensor network, Sequential estimation, Resampling and Signal processing in his study of Particle filter.
His Artificial intelligence research includes elements of Machine learning, Computer vision and Pattern recognition.
The study incorporates disciplines such as Likelihood function and Gaussian noise in addition to Mathematical optimization.
His most cited work include:
- Gaussian particle filtering (692 citations)
- Particle filtering (685 citations)
- Gaussian sum particle filtering (363 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Particle filter, Algorithm, Artificial intelligence, Monte Carlo method and Mathematical optimization.
The subject of his Particle filter research is within the realm of Tracking.
His Tracking study combines topics from a wide range of disciplines, such as Probabilistic logic and Real-time computing.
His studies deal with areas such as Probability distribution, Noise and Signal processing as well as Algorithm.
His biological study deals with issues like Pattern recognition, which deal with fields such as Maximum a posteriori estimation.
In his research, Petar M. Djuric performs multidisciplinary study on Mathematical optimization and Gaussian.
He most often published in these fields:
- Particle filter (39.96%)
- Algorithm (37.72%)
- Artificial intelligence (25.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (37.72%)
- Gaussian process (7.14%)
- Real-time computing (8.04%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Gaussian process, Real-time computing, Particle filter and Importance sampling.
His Algorithm research incorporates themes from Monte Carlo method, Bayesian probability and Flexibility.
His Monte Carlo method research integrates issues from Inference, Sampling, Probabilistic logic, Decoding methods and Coding.
Petar M. Djuric focuses mostly in the field of Real-time computing, narrowing it down to matters related to Radar and, in some cases, Trajectory.
His Particle filter research is multidisciplinary, incorporating elements of Convergence, Probability density function, Filter and Applied mathematics.
The various areas that Petar M. Djuric examines in his Importance sampling study include Autoencoder, Target distribution, Stochastic optimization and Clustering high-dimensional data.
Between 2018 and 2021, his most popular works were:
- Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking (17 citations)
- Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). (16 citations)
- Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling [Lecture Notes] (7 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
Petar M. Djuric mostly deals with Algorithm, Real-time computing, Radar, Tracking and Context.
His study in Algorithm is interdisciplinary in nature, drawing from both Monte Carlo method, Importance sampling, Filter and Bayesian probability.
His Filter research includes themes of Distributed algorithm, Particle filter, Computation and Probability mass function.
In his study, Auxiliary particle filter and Probability density function is strongly linked to Signal processing, which falls under the umbrella field of Particle filter.
His research in Real-time computing tackles topics such as Trajectory which are related to areas like Markov decision process, Perspective, Reflection mapping and Reinforcement learning.
His Tracking research is multidisciplinary, incorporating elements of Wavefront, Plane wave, Synchronization, Ranging and Near and far field.
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