Torch

Dr El Hadji Gning

Lecturer

Faculty and Department

  • Faculty of Science and Engineering
  • Department of Computer Science and Technology

Qualifications

  • PhD

Summary

I have a research background mainly in Bayesian state estimation applied to Robotics, Autonomous Driving cars, Aerospace and Transportation applications.

The originality of my work is on combining set-membership methods and Bayesian inference to obtain robust methods. Box particle filters is an example of Monte Carlo method resulting of this combination.

My research production is various and include: autonomous driving car, traffic flow estimation, sea border surveillance, intelligent transportation systems, unmanned autonomous systems (UAVs), multi target tracking algorithms and sensor networks.

Recent outputs

View more outputs

Journal Article

A box particle filter method for tracking multiple extended objects

De Freitas, A., Mihaylova, L., Gning, A., Schikora, M., Ulmke, M., Angelova, D., & Koch, W. (2019). A box particle filter method for tracking multiple extended objects. IEEE Transactions on Aerospace and Electronic Systems, 55(4), 1640 - 1655. https://doi.org/10.1109/TAES.2018.2874147

Analysis of the EPSRC Principles of Robotics in regard to key research topics

Gning, A., Davis, D. N., Cheng, Y., & Robinson, P. (2017). Analysis of the EPSRC Principles of Robotics in regard to key research topics. Connection Science, 29(3), 249-253. https://doi.org/10.1080/09540091.2017.1323456

Autonomous crowds tracking with box particle filtering and convolution particle filtering

De Freitas, A., Mihaylova, L., Gning, A., Angelova, D., & Kadirkamanathan, V. (2016). Autonomous crowds tracking with box particle filtering and convolution particle filtering. Automatica : the journal of IFAC, the International Federation of Automatic Control, 69, 380-394. https://doi.org/10.1016/j.automatica.2016.03.009

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

Mihaylova, L., Carmi, A. Y., Septier, F., Gning, A., Pang, S. K., & Godsill, S. (2014). Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, 1-16. https://doi.org/10.1016/j.dsp.2013.11.006

Box-particle probability hypothesis density filtering

Schikora, M., Gning, A., Mihaylova, L., Cremers, D., & Koch, W. (2014). Box-particle probability hypothesis density filtering. IEEE Transactions on Aerospace and Electronic Systems, 50(3), 1660-1672. https://doi.org/10.1109/taes.2014.120238

Research interests

Robotics, Artificial Intelligence, Statistical Signal Processing, Bayesian Inference, Data Fusion, Bayesian Networks, Monte Carlo Methods, Random Set Theory, Finite Set Statistics, Interval Analysis, Set-membership Methods, Imprecise Probability, Radar Theory.