Title: | Recursive Smoother Type Variable Splitting Methods for State Estimation |
Author(s): | Gao, Rui |
Date: | 2020 |
Language: | en |
Pages: | 91 + app. 64 |
Department: | Sähkötekniikan ja automaation laitos Department of Electrical Engineering and Automation |
ISBN: | 978-952-64-0144-7 (electronic) 978-952-64-0143-0 (printed) |
Series: | Aalto University publication series DOCTORAL DISSERTATIONS, 190/2020 |
ISSN: | 1799-4942 (electronic) 1799-4934 (printed) 1799-4934 (ISSN-L) |
Supervising professor(s): | Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland |
Thesis advisor(s): | Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland |
Subject: | Electrical engineering |
Keywords: | state estimation, sparsity, variable splitting, Bayesian filtering and smoothing, inequality constraint |
Archive | yes |
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Abstract:Many real-world applications in signal processing, such as target tracking, indoor positioning, and dynamic tomographic reconstruction, can be treated as state estimation problems for recovering the hidden states given a set of incomplete measurements. Mathematically, these problems can be formalized as a class of optimization problems which require minimization of composite functions, for example, the sum of quadratic functions and extra regularizers. A well-established way to solve the resulting problem is to decompose the composite function into separate sub-functions. Variable splitting methods such as the alternating direction method of multipliers are powerful batch optimization methods with such decomposition properties. However, the methods do not take the inherently temporal nature of the problems into account, which leads to bad computational and memory scaling when the number of time steps (e.g., millions) is in extreme scale.
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Description:The public defense will be organized via remote technology. Follow defence on 4.12.2020 12:00 – 15:00: https://aalto.zoom.us/j/67182642695
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Parts:[Publication 1]: Rui Gao, Filip Tronarp, Simo Särkkä. Iterated Extended Kalman Smoother-Based Variable Splitting for L1-Regularized State Estimation. IEEE Transactions on Signal Processing, Volume 67, Issue 19, pages 5078–5092, October 2019. DOI: 10.1109/TSP.2019.2935868 View at Publisher [Publication 2]: Rui Gao, Filip Tronarp, Simo Särkkä. Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. IEEE Signal Processing Letters, Volume 27, pages 1305–1309, July 2020. DOI: 10.1109/LSP.2020.3010159 View at Publisher [Publication 3]: Rui Gao, Filip Tronarp, Zheng Zhao, Simo Särkkä. Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. In IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, 6 pages, October 2019. DOI: 10.1109/MLSP.2019.8918821 View at Publisher [Publication 4]: Rui Gao, Simo Särkkä. Augmented Sigma-Point Lagrangian Splitting Method for Sparse Nonlinear State Estimation. In 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, pages 2090–2094, January 2021. DOI: 10.23919/Eusipco47968.2020.9287731 View at Publisher [Publication 5]: Rui Gao, Filip Tronarp, Simo Särkkä. Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. In 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, pages 1930–1934, September 2018. DOI: 10.23919/EUSIPCO.2018.8553122 View at Publisher [Publication 6]: Rui Gao, Simo Särkkä, Rubén Claveria-Vega, Simon Godsill. Autonomous Tracking and State Estimation with Generalized Group Lasso. Submitted to IEEE Transactions on Cybernetics, 11 pages, July 2020. https://arxiv.org/abs/2007.11573 |
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