Published in Petrophysics, 2020
An algorithm for performing autonomous geosteering using real time Gamma ray logs
Recommended citation: Veettil, D. R. A., & Clark, K. (2020, February 1). Bayesian Geosteering Using Sequential Monte Carlo Methods. Society of Petrophysicists and Well-Log Analysts. https://www.onepetro.org/journal-paper/SPWLA-2020-v61n1a4
Published in International conference on Information Fusion, 2019, Ottawa, Canada, 2019
Evaluation of the estimation performance of particle Gaussian mixture filters on a collection of benchmarking problems that have been selected from recent literature
Recommended citation: D. Raihan and S. Chakravorty, "Particle Gaussian Mixture Filters: Application and Performance Evaluation," 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada, 2019, pp. 1-8. https://ieeexplore.ieee.org/abstract/document/9011177
Published in International conference on Information Fusion, 2019, Ottawa, Canada, 2019
Optimal allocation of a single ground-based sensor to multiple space-based targets to maximize the information gained in the space situational awareness problem
Recommended citation: D. Raihan, W. Faber, S. Chakravorty and I. I. Hussein, "Sensor Scheduling Under Action Dependent Decision-Making Epochs," 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada, 2019, pp. 1-7. https://ieeexplore.ieee.org/document/9011443
Published in AAS/AIAA Astrodynamics Specialist Conference 2018. Snowbird-Utah., 2018
A sensor scheduling scheme that optimally chooses look directions to maximize the information gain
Recommended citation: Raihan, D., Faber, W., Chakravorty, S., Hussein, I. ”Parallel Markov Chain Monte Carlo for Sensor Scheduling”, Proceedings of AAS/AIAA Astrodynamics Specialist Conference 2018. Snowbird-Utah. https://ieeexplore.ieee.org/document/8455307
Published in International Conference on Information Fusion, 2018, Cambridge,UK, 2018
A nonliear filter that can handle the multimodality arising from the measurement function of the sensor
Recommended citation: D. Raihan and S. Chakravorty, "Particle Gaussian Mixture Filters-II," 2018 21st International Conference on Information Fusion (FUSION), Cambridge, 2018, pp. 1092-1099, doi: 10.23919/ICIF.2018.8455307 https://ieeexplore.ieee.org/document/8455307
Published in Automatica, 2018
A nonliear filter that can handle the multimodality arising from the measurement function of the sensor
Recommended citation: Raihan, D., Chakravorty, S. ”Particle Gaussian Mixture Filters-II”, Automatica, https://doi.org/10.1016/j.automatica.2018.07.024 https://doi.org/10.1016/j.automatica.2018.07.024
Published in Automatica, 2018
A nonliear filter that can handle the multimodality arising from the dynamics of the system
Recommended citation: Raihan, D., Chakravorty, S. Particle Gaussian Mixture Filters-I”, Automatica, https://doi.org/10.1016/j.automatica.2018.07.023 https://link.springer.com/article/10.1007/s40295-017-0114-8
Published in Journal of Astronautical Sciences, 2017
A Hybrid Filtering framework for spce object tracking under sparse measurements
Recommended citation: Raihan A. V, D., Chakravorty, S. An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking. J of Astronaut Sci 65, 111–134 (2018). https://doi.org/10.1007/s40295-017-0114-8 https://link.springer.com/article/10.1007/s40295-017-0114-8
Published in International Conference on Information Fusion, 2016, Heidelberg, Germany., 2015
A Bayesian filtering framework for handling multimodality and nonlinearity induced distortion.
Recommended citation: D. Raihan and S. Chakravorty, "Particle Gaussian Mixture (PGM) filters," 2016 19th International Conference on Information Fusion (FUSION), Heidelberg, 2016, pp. 1369-1376. https://ieeexplore.ieee.org/document/7528043
Published in AAS/AIAA Astrodynamics Specialist Conference 2015. Vail-Colorado, 2015
A filtering approach for tracking space objects when measurements are sparse.
Recommended citation: Raihan, D., Chakravorty, S. A UKF-PF based hybrid estimation scheme for space object tracking. Proceedings of AAS/AIAA Astrodynamics Specialist Conference 2015. Vail-Colorado. http://edplab.org/wp-content/uploads/2014/06/AASpap.pdf
Published in AIChE Journal, 2014
A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM).
Recommended citation: Sen, D., Raihan, D., & Chidambaram, M. Multiway continuous hidden Markov model based approach for fault detection and diagnosis. AIChE Journal. doi:10.1002/aic.14386. https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.14386