Pranab K. Mandal

dr.

20002019
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  • 3 Similar Profiles
State estimation Engineering & Materials Science
Metal forming Engineering & Materials Science
Industrial plants Engineering & Materials Science
Particle Filter Mathematics
Optimal Filter Mathematics
Particle Filtering Mathematics
Force measurement Engineering & Materials Science
Signal detection Engineering & Materials Science

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Research Output 2000 2019

3 Downloads (Pure)

Bayesian Model-based State Estimation for Mass Production Metal Forming

Havinga, J., Mandal, P. K. & van den Boogaard, A. H., 2019, 38th International Deep Drawing Research Group Conference. 1 ed. IOP Science, p. 1-8 8 p. 012095. (IOP Conference Series: Materials Science and Engineering; vol. 651, no. 012095).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Force measurement
Metal forming
State estimation
Friction materials
Lubrication
12 Downloads (Pure)
Open Access
File
Metal forming
State estimation
Industrial plants
Interpolation
Physics
23 Downloads (Pure)

Optimal Particle-Filter-Based Detector

Boers, Y. & Mandal, P. K., 1 Mar 2019, In : IEEE signal processing letters. 26, 3, p. 435 - 439 5 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Optimal Filter
Signal detection
Particle Filter
Likelihood Ratio
Detector
1 Citation (Scopus)
18 Downloads (Pure)

Riemann–Langevin Particle Filtering in Track-Before-Detect

Iglesias Garcia, F., Mandal, P. K., Bocquel, M. & Garcia Marques, A., Jul 2018, In : IEEE signal processing letters. 25, 7, p. 1039-1043 5 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Particle Filtering
Filtering
Control nonlinearities
On-line Estimation
State estimation
1 Downloads (Pure)

A Two-stage Particle Filter in High Dimension

Wang, W. & Mandal, P. K., 2017, 20th International Conference on Information Fusion: 2017 Proceedings. IEEE, 8 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Monte Carlo methods