Faculty

Kundan Kumar

Assistant Professor

PhD (IIT Patna)

+91.79.61911178

https://www.researchgate.net/profile/Kundan-Kumar-20?ev=hdr_xprf

Research Interests: Bayesian Filtering and Smoothing, Estimation and Control, Statistical Signal Processing, State and Parameter Estimation, Sensor Fusion, Physics-Informed Machine Learning

Profile

Professor Kumar received his BTech degree in Electrical Engineering from Muzaffarpur Institute of Technology, Muzaffarpur, in 2016. He subsequently earned his PhD degree in Electrical Engineering from the Indian Institute of Technology Patna in 2023. Following his PhD synopsis seminar, he served as a Teaching Fellow in the School of Electrical and Electronics Engineering at Vellore Institute of Technology, Bhopal. Later, he was a Postdoctoral Researcher in the Department of Electrical Engineering and Automation at Aalto University, Finland.

Professor Kumar’s research interests include Bayesian filtering and smoothing for centralised and distributed state-space models, estimation in cyber-physical systems, target tracking, and physics-informed machine learning. He has published 16 peer-reviewed scientific articles in international journals and conference proceedings. Additionally, he is an active member of the academic community, serving as a reviewer for several high-impact journals and conferences, including IEEE Transactions on Automatic Control, IEEE Transactions on Vehicular Technology, IEEE Transactions on Aerospace and Electronic Systems, IEEE Signal Processing Letters, International Conference on Artificial Intelligence and Statistics, and International Conference on Acoustics, Speech, and Signal Processing.

Research

Development of computationally efficient and accurate state estimation algorithms
Distributed estimation algorithms for linear and nonlinear state space models
Physics-informed machine learning from state and parameter estimation perspective
Single and multi-target tracking for centralised and distributed state space models
Centralised and distributed estimation in cyber-physical systems
Outlier detection methods in time series data
Hardware implementation on the drone or the robot car
State estimation in energy systems such as electric vehicles and smart grid

Publications

Journal Publications

Iqbal, M., Kumar, K., and Särkkä, S. (2025). Communication-efficient distributed Kalman filter using ADMM. IEEE Transactions on Automatic Control, 71(3), 1916–1923. https://doi.org/10.1109/TAC.2025.3615237
Das, S., Kumar, K., and Bhaumik, S. (2024). Passive underwater tracking with unknown measurement noise statistics using variational Bayesian approximation. Digital Signal Processing, 153, Article 104648. https://doi.org/10.1016/j.dsp.2024.104648
Kumar, K., Das, S., and Bhaumik, S. (2023). A new method for nonlinear state estimation problem. Digital Signal Processing, 132, Article 103788. https://doi.org/10.1016/j.dsp.2022.103788
Kumar, K., Tiwari, R. K., Bhaumik, S., and Date, P. (2023). Polynomial chaos Kalman filter for target tracking applications. IET Radar, Sonar & Navigation, 17(2), 247–260. https://doi.org/10.1049/rsn2.12338
Kumar, K., Bhaumik, S., and Arulampalam, S. (2022). Tracking an underwater object with unknown sensor noise covariance using orthogonal polynomial filters. Sensors, 22(13), Article 4970. https://doi.org/10.3390/s22134970
Kumar, K., Bhaumik, S., and Date, P. (2021). Extended Kalman filter using orthogonal polynomials. IEEE Access, 9, 59675–59691. https://doi.org/10.1109/ACCESS.2021.3073289

Conference Proceedings

Kumar, K., Iqbal, M., and Särkkä, S. (2025). Statistical linear regression approach to Kalman filtering and smoothing under cyber-attacks. 2025 33rd European Signal Processing Conference (EUSIPCO). https://doi.org/10.23919/EUSIPCO63237.2025.11226213
Gasmi, E., Kumar, K., Sid, M. A., and Särkkä, S. (2025). Event-triggered particle filter with one-step randomly delayed measurement. 2025 33rd European Signal Processing Conference (EUSIPCO). https://doi.org/10.23919/EUSIPCO63237.2025.11226129
Kumar, K., Iqbal, M., and Särkkä, S. (2024). Risk-sensitive filtering under false data injection attacks. 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). https://doi.org/10.1109/MFI62651.2024.10705765
Kumar, K., and Särkkä, S. (2024). Polynomial chaos expansion based Rauch–Tung–Striebel smoothers. 2024 27th International Conference on Information Fusion (FUSION). https://doi.org/10.23919/FUSION59988.2024.10706310
Das, S., Kumar, K., and Bhaumik, S. (2023). Bearing only tracking with speed and range constraints. 2023 31st European Signal Processing Conference (EUSIPCO). https://doi.org/10.23919/EUSIPCO58844.2023.10289720
Das, S., Kumar, K., Radhakrishnan, R., and Bhaumik, S. (2022). Bearing-only tracking using range-parameterized shifted Rayleigh filter. Proceedings of OCEANS 2022. https://doi.org/10.1109/OCEANSChennai45887.2022.9775468
Kumar, K., and Bhaumik, S. (2020). Nonlinear filter for a system with randomly delayed measurements and inputs. 7th International Electronic Conference on Sensors and Applications. https://doi.org/10.3390/ecsa-7-08236
Kumar, K., and Bhaumik, S. (2018). Higher degree cubature quadrature Kalman filter for randomly delayed measurements. 2018 21st International Conference on Information Fusion (FUSION). https://doi.org/10.23919/ICIF.2018.8455865
Singh, A. K., Kumar, K., and Bhaumik, S. (2018). Cubature and quadrature based continuous-discrete filters for maneuvering target tracking. 2018 21st International Conference on Information Fusion (FUSION). https://doi.org/10.23919/ICIF.2018.8455253
Singh, A. K., Bhaumik, S., and Kumar, K. (2018). Cubature quadrature filter for one-step randomly delayed measurements. 2018 Indian Control Conference (ICC). https://doi.org/10.1109/INDIANCC.2018.8307950