English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We address the problem of autonomous tracking and state estimation for marinevessels, autonomous vehicles, and other dynamic signals under a (structured)sparsity assumption. The aim is to improve the tracking and estimation accuracywith respect to classical Bayesian filters and smoothers. We formulate theestimation problem as a dynamic generalised group Lasso problem and develop aclass of smoothing-and-splitting methods to solve it. The Levenberg--Marquardtiterated extended Kalman smoother-based multi-block alternating directionmethod of multipliers (LM-IEKS-mADMM) algorithms are based on the alternatingdirection method of multipliers (ADMM) framework. This leads to minimisationsubproblems with an inherent structure to which three new augmented recursivesmoothers are applied. Our methods can deal with large-scale problems withoutpre-processing for dimensionality reduction. Moreover, the methods allow one tosolve nonsmooth nonconvex optimisation problems. We then prove that under mildconditions, the proposed methods converge to a stationary point of theoptimisation problem. By simulated and real-data experiments includingmulti-sensor range measurement problems, marine vessel tracking, autonomousvehicle tracking, and audio signal restoration, we show the practicaleffectiveness of the proposed methods.

Autonomous Tracking and State Estimation with Generalised Group Lasso