# Autonomous Tracking and State Estimation With Generalized Group Lasso.

@article{Gao2021AutonomousTA, title={Autonomous Tracking and State Estimation With Generalized Group Lasso.}, author={Rui Gao and Simo Sarkka and Rub'en Claveria-Vega and Simon J. Godsill}, journal={IEEE transactions on cybernetics}, year={2021}, volume={PP} }

We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The aim is to improve the tracking and estimation accuracy with respect to the classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalized group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg-Marquardt iterated extended Kalman…

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## References

SHOWING 1-10 OF 46 REFERENCES

Estimation with Applications to Tracking and Navigation

- Mathematics, Computer Science
- 2001

Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.

The Iterated Kalman Smoother as a Gauss-Newton Method

- MathematicsSIAM J. Optim.
- 1994

The iterated Kalman smoother is presented and shown to be a Gauss–Newton method for maximizing the likelihood function in the nonaffine case and to decompose a large least squares problem into a sequence of much smaller problems.

Bayesian Filtering and Smoothing

- Computer ScienceInstitute of Mathematical Statistics textbooks
- 2013

This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework and learns what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

- Computer ScienceFound. Trends Mach. Learn.
- 2011

It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

Numerical Optimization

- Computer Science
- 1999

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in…

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

- Computer ScienceIEEE Transactions on Cybernetics
- 2016

This paper proposes intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can drastically reduce the time and effort required to successfully complete an in-vehicle selection task.

Bayesian variable selection and regularization for time–frequency surface estimation

- Mathematics
- 2004

Summary. We describe novel Bayesian models for time–frequency inverse modelling of non‐stationary signals. These models are based on the idea of a Gabor regression, in which a time series is…

Levenberg-Marquardt and Line-Search Extended Kalman Smoothers

- MathematicsICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2020

Levenberg-Marquardt and line-search extensions of the classical iterated extended Kalman smoother (IEKS) which has previously been shown to be equivalent to the Gauss-Newton method are presented.

Cooperative localisation using posterior linearisation belief propagation

- Computer Science
- 2019

This paper presents the posterior linearisation belief propagation (PLBP) algorithm for cooperative localisation in wireless sensor networks with nonlinear measurements and shows by numerical simulations how PLBP can outperform other algorithms in the literature.

Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method

- Computer Science, Mathematics2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
- 2019

A new augmented Lagrangian Kalman smoother method is developed for solving the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L_{1} -$regularization, where the primal variable update is reformulated asKalman smoother.