TR2023-086
A Generalized GraphEM for Sparse Time-Varying Dynamical Systems
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- "A Generalized GraphEM for Sparse Time-Varying Dynamical Systems", World Congress of the International Federation of Automatic Control (IFAC), Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita, Eds., DOI: 10.1016/j.ifacol.2023.10.630, July 2023, pp. 5957-5962.BibTeX TR2023-086 PDF
- @inproceedings{Greiff2023jul2,
- author = {Greiff, Marcus and Di Cairano, Stefano and Mansour, Hassan and Berntorp, Karl},
- title = {A Generalized GraphEM for Sparse Time-Varying Dynamical Systems},
- booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
- year = 2023,
- editor = {Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita},
- pages = {5957--5962},
- month = jul,
- publisher = {Elsevier},
- doi = {10.1016/j.ifacol.2023.10.630},
- issn = {2405-8963},
- url = {https://www.merl.com/publications/TR2023-086}
- }
,
- "A Generalized GraphEM for Sparse Time-Varying Dynamical Systems", World Congress of the International Federation of Automatic Control (IFAC), Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita, Eds., DOI: 10.1016/j.ifacol.2023.10.630, July 2023, pp. 5957-5962.
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MERL Contacts:
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Research Areas:
Abstract:
We consider the problem of joint parameter estimation and smoothing in structured linear systems using the expectation maximization (EM) framework. Specifically, we explore how partially known sparsity structures in the estimation model can be leveraged to improve the computation speed and performance of the considered EM approaches. We use these ideas to generalize a recently proposed GraphEM algorithm to a linear time-varying setting, where the sparsity structures may vary in time. We obtain a biconvex form of the majorizing function in the M -step, which is minimized subject to an `1-regularization using a Douglas-Rachford proximal splitting algorithm. Numerical results using a satellite positioning example shows significant improvements in the estimation errors and an F1-score that quantifies model sparsity.
Related News & Events
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NEWS MERL presents 9 papers at 2023 IFAC World Congress Date: July 9, 2023 - July 14, 2023
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Diego Romeres; Abraham P. Vinod
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
MERL's contributions covered topics including decision-making for autonomous vehicles, statistical and learning-based estimation for GNSS and energy systems, impedance control for delta robots, learning for system identification of rigid body dynamics and time-varying systems, and meta-learning for deep state-space modeling using data from similar systems. The invited session (MERL co-organizer: Ankush Chakrabarty) was on the topic of “Estimation and observer design: theory and applications” and the workshop (MERL co-organizer: Karl Berntorp) was on “Gaussian Process Learning for Systems and Control”.
- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.